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To evaluate if patient-derived organoids (PDOs) may predict response to neoadjuvant (NAT) chemotherapy in patients with pancreatic adenocarcinoma.PDOs have been explored as a biomarker of therapy response and for personalized therapeutics in patients with pancreatic cancer.During 2017-2021, patients were enrolled into an IRB-approved protocol and PDO cultures were established. PDOs of interest were analyzed through a translational pipeline incorporating molecular profiling and drug sensitivity testing.One hundred thirty-six samples, including both surgical resections and fine needle aspiration/biopsy from 117 patients with pancreatic cancer were collected. This biobank included diversity in stage, sex, age, and race, with minority populations representing 1/3 of collected cases (16% Black, 9% Asian, 7% Hispanic/Latino). Among surgical specimens, PDO generation was successful in 71% (15 of 21) of patients who had received NAT prior to sample collection and in 76% (39 of 51) of patients who were untreated with chemotherapy or radiation at the time of collection. Pathological response to NAT correlated with PDO chemotherapy response, particularly oxaliplatin. We demonstrated the feasibility of a rapid PDO drug screen and generated data within 7 days of tissue resection.Herein we report a large single-institution organoid biobank, including ethnic minority samples. The ability to establish PDOs from chemotherapy-naive and post-NAT tissue enables longitudinal PDO generation to assess dynamic chemotherapy sensitivity profiling. PDOs can be rapidly screened and further development of rapid screening may aid in the initial stratification of patients to the most active NAT regimen.

Introduction: COVID-19 vaccination substantially reduces morbidity and mortality associated with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and severe illness. However, despite effective COVID-19 vaccines many questions remain about the efficacy of vaccines and the durability and robustness of immune responses, especially in immunocompromised persons. The NCI-funded Serological Sciences Network (SeroNet) is a coordinated effort including 11 sites to advance research on the immune response to SARS-CoV-2 infection and COVID-19 vaccination among diverse and vulnerable populations. The goals of the Pooling Project are: (1) to conduct real-world data (RWD) analyses using electronic medical records (EMR) data from four health care systems (Kaiser Permanente Northern California, Northwell Health, Veterans Affairs-Case Western, and Cedars-Sinai) to determine vaccine effectiveness in (a) cancer patients; (b) autoimmune diseases and (c) solid organ transplant recipients (SOTR); (2) to conduct meta-analyses of prospective cohort studies from eight SeroNet institutions (Cedars-Sinai, Johns Hopkins, Northwell Health, Emory University, University of Minnesota, Mount Sinai, Yale University) to determine post-vaccine immune responses in (a) lung cancer patients; (b) hematologic cancers/hematopoietic stem cell transplant (HSCT) recipients; (c) SOTR; (d) lupus. Methods: For our RWD analyses, data is extracted from EMR using standardized algorithms using ICD-10 codes to identify immunocompromised persons (hematologic and solid organ malignancy; SOTR; autoimmune disease, including inflammatory bowel disease, rheumatoid arthritis, and SLE). We use common case definitions to extract data on demographic, laboratory values, clinical co-morbidity, COVID-19 vaccination, SARS-CoV-2 infection and severe COVID-19, and disease-specific variables. In addition, we pool individual-level data from prospective cohorts enrolling patients with cancer and other immunosuppressed conditions from across network. Surveys and biospecimens from serology and immune profiling are collected at pre-specified timepoints across longitudinal cohorts. Results: Currently, we have EMR data extracted from 4 health systems including >715,000 cancer patients, >9,500 SOTR and >180,000 with autoimmune conditions. Prospective cohorts across the network have longitudinal data on >450 patients with lung cancer, >1,200 patients with hematologic malignancies, >400 SOTR and >400 patients with lupus. We will report results examining vaccine effectiveness for prevention of SARS-CoV-2 infection, severe COVID-19 and post-acute sequelae of COVID-19 (PAS-C or long COVID) in cancer patients compared to other immunocompromised conditions. Conclusion: Our goal is to inform public health guidelines on COVID-19 vaccine and boosters to reduce SARS-CoV-2 infection and severe illness in immunocompromised populations. Citation Format: Elham Kazemain, Jane Figueiredo, Jacek Skarbinski, Russell McBride, Viviana Simon, Amy B. Karger, F. Eun-Hyung Lee, Fred R. Hirsch, Andrea Cox, Sabra Klein, Rong Fan, Stephanie Halene, David A. Zidar, James M. Crawford, Bharat Thyagarajan, Charles Gleason, Alex Mathson, Komal Srivastava, Puleng Moshele, Toby Amoss, Martin Runnstrom, Susanne Linderman, Ananda M. Rodilla, Philip C. Mack, Yu Shyr, Anna Yin, Patrick Shea, Jennifer VanOudenhove, Hinnah Siddiqui, Brigid M. Wilson, Eric P. Elkin, Crystal A. Hsiao, Yonah Ziemba, Cheryl B. Schleicher, Sharon Fox, Lawrence H. Kushi, Karen Reckamp, Akil Merchant, Noah Merin. SeroNet Pooling Project of immunocompromised populations [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 798.

The purpose of this study was to improve the identification and solicitation of human subject candidates to augment patient enrollment to clinical trials. In the past, our enrollment efforts involved manual mechanisms that began at the time that cancer patients were presented for care, most commonly at our chemotherapy clinics. We reasoned that computational review of pathology records may be far more efficient, allowing us to screen larger numbers of patients, and may allow us to intercept patients at the time of diagnosis, rather than time of care. We thus implemented a new workflow: (1) computational query of pathology database to filter candidates based on key eligibility criteria; (2) validation of the query using a clinical research coordinator, who can then (3) navigate the patient to the trial. As a proof of concept, starting in August of 2022, we selected two gastrointestinal oncology trials, the COBRA and PACES trial, based on trial eligibility that could be easily extracted using pathologic criteria, and because these trials had low enrollment. We designed computational queries to extract cohorts of potential patients whose surgery dates and pathology staging identified potential clinical trial candidates. A coordinator performed chart-review to validate these candidates. The trial coordinator then contacted the patient’s surgical oncologist, facilitated referral to a medical oncologist, and tracked the number of patients approached and consented. Whereas in a 12-month period preceding this new workflow, we had 0 and 1 patients approached for the PACES and COBRA trial, in the 3-month period following the new intervention, we had 8 and 2 patients approached. These data suggest computational cohort building and subsequent navigation have the potential to improve enrollment onto clinical trials and motivate extension of this work to additional trials. Table(s) Trial # of patients approached pre-intervention Computational Criteria Meeting computational criteria Coordinator validation criteria # of patients validated by coordinator # of patients connected with medical oncologist # of patients approached post-intervention PACES 0 Colorectal cancer, stage T0-T3,N0-1 Date Aug 2021 – July 2022 23 Facility location, No evidence of disease on colonoscopy performed 120 -456 days post-surgery, No chemotherapy, No other malignancy 18 8 8 COBRA 1 Colorectal cancer pT3N0resection date August 2022 – October 2022 18 ≥ 12 lymph nodes assessedPatient locationIf the subject appropriate for active surveillanceSurgery within 14-60 days of randomization 4 0 2 Citation Format: Yonah Ziemba, Kaitlin Parnahay, Paul Ward, Nalan Yurtsever, Sharon Fox, Cheryl Schleicher, Priya Singh-Shiwsankar, Vincent Vinciguerra, Joseph Herman, James M. Crawford, Daniel A. King. Computational cohort discovery and coordinator-led validation to navigate potential research candidates to clinical trials [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3150.

Despite the benefits of digital pathology, data storage and management of digital whole slide images introduces new logistical and infrastructure challenges to traditionally analog pathology labs.Our goal was to analyze pathologist slide diagnosis patterns to determine the minimum number of pixels required during the diagnosis.We developed a method of using pathologist viewing patterns to vary digital image resolution across virtual slides, which we call variable resolution images. An additional pathologist reviewed the variable resolution images to determine if diagnoses could still be rendered.Across all slides, the pathologists rarely zoomed in to the full resolution level. As a result, the variable resolution images are significantly smaller than the original whole slide images. Despite the reduction in image sizes, the final pathologist reviewer could still proide diagnoses on the variable resolution slide images.Future studies will be conducted to understand variability in resolution requirements between and within pathologists. These findings have the potential to dramatically reduce the data storage requirements of high-resolution whole slide images.

Little information is available on the rates of adoption or foster care in specific pediatric disorders. In a survey of 14 groups of patients seen for different pediatric disorders, 7 showed a significant elevation of adoption or foster care rates. This finding is discussed in relationship t