Citations

Abstract Pancreatic ductal adenocarcinoma (PDAC) is predicted to be the second cause of cancer death in the US by 2040. Five-year overall survival is only ~12% despite decades of research. The mainstay of treatment remains chemotherapy in the absence of highly recurrent, therapeutically actionable, genetic targets. For the minority of patients (~15-20%) who are candidates for curative-intent surgical resection, six months of intensive neoadjuvant chemotherapy with/without chemoradiation has become standard-of-care. However, despite evidence of radiographic, metabolic, and biochemical responses, significant viable residual disease on pathologic analysis is almost universally observed with approximately 40-60% risk of disease recurrence. Thus, there is a critical need to better understand why PDAC remains recalcitrant to current therapeutic modalities. Pathologic evaluation of PDAC reveals a complex microenvironment consisting of neoplastic cells dynamically interacting with both “normal” cellular (e.g. mesenchymal, immune) and non-cellular (e.g. collagen) stromal elements. While biologically important, experimental models and small-scale efforts to dissect the PDAC ecosystem are limited by focus on individual tumor compartments and generation of conflicting results. Consequently, there is a need for a more comprehensive understanding of the structure and dynamics of the PDAC TME through the theoretical lens of evolutionary ecology. Current therapies are not focused on the PDAC ecosystem. We hypothesize that cell-cell interactions of PDAC cells with its local niche are important for conferring treatment resistance. Thus, better understanding of said interactions could yield novel ecologically-informed therapies. Consequently, we developed a digital pathology library of approximately 1000 PDAC resection cases (whole-slide H&E images) performed at Mayo Clinic between 2013-2021. Approximately 400 cases are treatment naive while about 600 were treated with neoadjuvant therapy. While some qualitative features of pathologic analysis have prognostic value (e.g., pathologic treatment response, presence of perineural or lymphovascular invasion, extent of desmoplasia), we aim to identify prognostic spatial relationships using tools from landscape ecology. We utilized the HoVer-Net model for nuclei segmentation and classification and spatial statistics methods and landscape fragmentation analysis to understand the spatial organization within the PDAC tumor ecology. With these methodologies, we can assess the spatial context of cell interactions, habitat/niche/microenvironment formation, ecological relationships between cell populations, and the different tissue components. The different spatial statistics/metrics are used to quantify levels of immune cell infiltration, mixing, as well as the complexity and diversity of microenvironment phenotypes leading to high levels of tumor heterogeneity. Such measurements can help predict the likelihood of a PDAC tumor adaptive potential that could lead to fast evolutionary responses to therapy, progression, and poor prognosis. Citation Format: Merih D. Toruner, Luis Cisneros, Zafar Siddiqui, Chady Meroueh, Ritambhara Singh, Carlo Maley, Martin E. Fernandez-Zapico, Ryan M. Carr. Dissecting the pancreatic cancer microenvironment using landscape ecology [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Cancer Evolution and Data Science: The Next Frontier; 2023 Dec 3-6; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_2):Abstract nr B026.

Abstract Glioblastoma is a uniquely challenging and aggressive cancer that is practically considered uniformly fatal. This aggressiveness is driven by heterogeneity between and within patients and the diffuse invasion of tumor cells deep into the normal appearing brain tissue surrounding the frank tumor abnormality. We use our unique collection of 1000+ image-localized biopsies across nearly 200 patients to detect sex-distinct patterns of mapping between regional imaging features and underlying regional tumor biology. Specifically, we build mathematical models that fuse locoregional changes on MRI to underlying changes in tumor biology. We apply the resultant spatio-temporal models to serial imaging of patients receiving a variety of standard of care and novel therapeutics to track tumor dynamics under said therapies. This allows us unprecedent insight into the dynamics of each patients tumor under therapy. This includes detecting clonal loss through targeted therapies as well as shifts in the tumor ecosystem under immunotherapies. In this presentation, I will demonstrate how these methods can be brought together to help us detect, decipher and predict tumor dynamics and evolution in patients. Specifically, I will demonstrate the value of these novel combined resources in predicting response to treatment for EGFR-targeted therapies in 3 clinical trials and differentiating response to novel dendritic cells vaccines and CAR T-cell therapies in 2 additional clinical trials. Taken together, these complimentary approaches provide a platform to incorporate inter- and intra- tumoral heterogeneity into accurate predictive models that advance evolutionary- and ecologically-adaptive precision oncology for glioblastoma patients. Citation Format: Kristin R. Swanson. Composing each patient's equation: Deciphering brain tumor dynamics and evolution by integrating multi-regional image-localized biopsies [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Cancer Evolution and Data Science: The Next Frontier; 2023 Dec 3-6; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_2):Abstract nr IA003.

Background Research is limited investigating injuries/illness incidence among National Hockey League (NHL) players. This study sought to establish injury/illness incidence, initial versus subsequent injury risk among NHL players, and determine temporal trends of injury and illness incidence. Hypothesis Variations in injury incidence by body region, and initial versus subsequent injury would be observed among positions. Study Design Retrospective cohort study Methods Publicly available data were utilized. NHL players 18 years or older between 2007-2008 to 2018-2019 were included. Injury and illness was stratified by position and body segment. Incidence rate (IR), and initial versus subsequent injury and illness risk ratios were calculated. Temporal trends were reported. Results Nine thousand, seven-hundred and thirty four injuries and illnesses were recorded. Centers had the highest overall IR at 15.14 per 1000 athlete game exposures (AGEs) (95%CI:15.12-15.15) and were 1.4 times more likely to sustain a subsequent injury compared to other positions. The groin/hip/thigh was the most commonly injured body region with an IR of 1.14 per 1000 AGEs (95%CI:1.06-1.21), followed by the head/neck (0.72 per 1000 AGEs, 95%CI:0.66-0.78). Combined injury and illness IR peaked in 2009-2010 season at 12.01 (95%CI: 11.22-12.79). The groin/hip/thigh demonstrated peak incidence during the 2007-2008 season (2.53, 95%CI:2.17-2.90); head/neck demonstrated a peak incidence in 2010-2011 season (Overall: 1.03, 95%CI:0.81-1.26). Injuries reported as ‘lower body’ increased over time. Conclusions Positional differences were observed; centers demonstrated the highest overall IR, and subsequent injury risk. Injury by body region was similar to previous literature. Head/neck and concussion decreased over time supporting rule changes in body checking and visor wear. Clinicians should be aware that ‘lower body’ injuries increased over time; therefore, injuries to the groin/hip/thigh or knee are likely underreported. Level of Evidence Level 3

Abstract Many germline BRCA2 missense variants of uncertain significance (VUS) remain to be clinically classified as pathogenic or benign. Here we identify 70 missense VUS in the DNA binding domain (DBD) of BRCA2 that partially reduced homologous recombination DNA repair activity of the protein in a homology directed DNA repair cell-based assay. CRISPR/cas9-based knockin of these variants reduced cell survival of haploid cells and conferred increased sensitivity to the PARP inhibitor Olaparib. Sequence alignment analysis found that the variants were highly conserved across species from pufferfish to human. Structural analysis showed that these variants are located predominantly in unstructured regions of the DBD. Case-control association studies showed that the hypomorphic variants conferred moderate risks of breast (OR:2.85; 95%CI:1.43-5.88) and ovarian cancer (OR:3.37; 95%CI:1.30-8.54) that were significantly different from risks associated with known pathogenic and benign variants. Cumulative lifetime risks of breast and ovarian cancer among carriers were estimated at 25% and 4.7%, respectively. Age at diagnosis of breast cancer among hypomorphs carriers was 55 years of age as opposed to 48 years of age for carriers of pathogenic variants. In addition, the combination of functional and other genetic data in an ACMG/AMP-like variant classification model reclassified 39 variants as likely pathogenic. This novel group of low penetrance missense variants further calibrate BRCA2 variant classification methods and have significant implications for the management of variant carriers. Citation Format: Huaizhi (Gilbert) Huang, Chunling Hu, Jie Na, Mohamed Abozaid, Amik Munankarmy, Tara Rao, Carolyn A. Dunn Lumby, Paulo Cilas Morais Lyra, Ronan E. Couch, Benjamin R. Persons, Eric C. Polley, Rachid Karam, Tina Pesaran, Siddhartha Yadav, Alvaro N.A. Monteiro, Nicholas J. Boddicker, Susan M. Domchek, Marcy E. Richardson, Fergus J. Couch. Functional and clinical characterization of hypomorphic missense variants in the BRCA2 cancer predisposition gene [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Breast Cancer Research; 2023 Oct 19-22; San Diego, California. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_1):Abstract nr PR09.

Teamwork in graduate medical education (GME) is often hindered in clinical learning environments where discontinuity among residents, supervisors, and other health care professionals is typical. Teaming is a conceptual approach to teamwork in dynamic environments with constantly changing team members and goals. Teaming is built on principles of project management and team leadership, which together provide an attractive strategy for addressing teamwork challenges in GME. Indeed, teaming is now a requirement of the Accreditation Council for Graduate Medical Education Clinical Learning Environment Review program. However, many clinician-educators and leaders may be unfamiliar with teaming and how to integrate it into their GME programs. In this article, the teaming framework is described with a specific example of how it can be applied to improve hospital ward rounds, a common setting of teamwork breakdown. The goal of this article is to educate and encourage GME leaders as they learn new ways to implement teaming to improve patient care and education in their programs.

AbstractPredicting who will benefit from treatment with immune checkpoint inhibition (ICI) in patients with advanced melanoma is challenging. We developed a multivariable prediction model for response to ICI, using routinely available clinical data including primary melanoma characteristics. We used a population‐based cohort of 3525 patients with advanced cutaneous melanoma treated with anti‐PD‐1‐based therapy. Our prediction model for predicting response within 6 months after ICI initiation was internally validated with bootstrap resampling. Performance evaluation included calibration, discrimination and internal–external cross‐validation. Included patients received anti‐PD‐1 monotherapy (n = 2366) or ipilimumab plus nivolumab (n = 1159) in any treatment line. The model included serum lactate dehydrogenase, World Health Organization performance score, type and line of ICI, disease stage and time to first distant recurrence—all at start of ICI—, and location and type of primary melanoma, the presence of satellites and/or in‐transit metastases at primary diagnosis and sex. The over‐optimism adjusted area under the receiver operating characteristic was 0.66 (95% CI: 0.64–0.66). The range of predicted response probabilities was 7%–81%. Based on these probabilities, patients were categorized into quartiles. Compared to the lowest response quartile, patients in the highest quartile had a significantly longer median progression‐free survival (20.0 vs 2.8 months; P < .001) and median overall survival (62.0 vs 8.0 months; P < .001). Our prediction model, based on routinely available clinical variables and primary melanoma characteristics, predicts response to ICI in patients with advanced melanoma and discriminates well between treated patients with a very good and very poor prognosis.

AbstractWe present three cases of acute coronary obstruction postsurgical repair of type A aortic dissection, which were successfully treated with percutaneous coronary intervention. We describe a step‐by‐step approach to performing percutaneous coronary intervention in selective cases of coronary obstruction related to type A aortic dissection.

AbstractINTRODUCTIONThis study evaluated the performance of the Lumipulse plasma beta‐amyloid (Aβ) 42/40 and pTau181 compared to other assays to detect an abnormal amyloid‐positron emission tomography (PET).METHODSPlasma samples from cognitively unimpaired (N = 179) and MCI/AD dementia (N = 36) individuals were retrospectively evaluated. Plasma Aβ42/40 and pTau181 were measured using the Lumipulse and Simoa immunoassays. An immunoprecipitation mass spectrometry (IP‐MS) assay for plasma Aβ42/40 was also evaluated. Amyloid‐PET status was the outcome measure.RESULTSLumipulse and IP‐MS Aβ42/40 exhibited the highest diagnostic accuracy for detecting an abnormal amyloid‐PET (areas under the curve [AUCs] of 0.81 and 0.84, respectively). The Lumipulse and Simoa pTau181 assays exhibited lower performance (AUCs of 0.74 and 0.72, respectively). The Simoa Aβ42/40 assay demonstrated the lowest diagnostic accuracy (AUC 0.57). Combining Aβ42/40 and pTau181 did not significantly improve performance over Aβ42/40 alone for Lumipulse (AUC 0.83) or over pTau181 alone for Simoa (AUC 0.71)DISCUSSIONThe Lumipulse Aβ42/40 assay showed similar performance to the IP‐MS Aβ42/40 assay for detection of an abnormal amyloid‐PET; and both assays performed better than the two p‐tau181 immunoassays. The Simoa Aβ42/Aβ40 assay was the least accurate at predicting an abnormal amyloid‐PET status.Highlights Lumipulse plasma Aβ42/Aβ40 AUC for abnormal amyloid‐PET detection was 0.81. This performance was comparable to previously reported IP‐MS and higher than Simoa. Performance of Alzheimer's disease blood biomarkers varies between assays.