Citations

ABSTRACT The HLA‐C*14:84 allele is characterised by a single nucleotide substitution in codon 181 in exon 3.

Abstract Despite long-standing interest in combining topoisomerase I (TOP1) poisons with PARP inhibitors (PARPis), the mechanistic basis for the variable and often unpredictable response to this strategy remains unclear. Early preclinical studies suggested that PARP inhibition amplifies TOP1 poison-induced DNA damage, implying broad therapeutic potential; however, emerging translational and clinical data—particularly in ovarian cancer—indicate that synergy is context-dependent and governed by tumor-intrinsic features that have not been fully defined.Here we assessed determinants of response in five HR proficient ovarian cancer (OC) patient-derived xenografts (PDXs) previously treated with the SN-38 prodrug etirinotecan pegol (NKTR-102) in combination with the PARPi rucaparib (C. Nitschmann, Clin. Cancer Res. 22(2) Suppl. B50, 2016). Although all models were refractory to single-agent rucaparib, 50% exhibited enhancement of NKTR-102-induced tumor regression with the addition of rucaparib. Neither PARP1 abundance nor extent of rucaparib-mediated poly(ADP-ribose) suppression correlated with therapeutic benefit. Instead, sensitization was restricted to PDXs intrinsically responsive to NKTR-102 and was strongly associated with high levels of NKTR-102-induced TOP1-DNA covalent complexes. In further analysis, 2 of 3 resistant PDXs exhibited high levels of the ABC transporter ABCG2 despite lack of previous therapy, revealing a previously underappreciated mechanism of de novo resistance that limits effective TOP1 inhibitor activity independent of homologous recombination status. Notably, NKTR-102 response did not track with platinum/taxane sensitivity, highlighting the distinct molecular circuitry governing TOP1 poison susceptibility.Collectively, these findings demonstrate that rucaparib can potentiate NKTR-102-induced regression in tumors capable of forming abundant TOP1-DNA covalent complexes. This work identifies TOP1-DNA complex trapping and lack of ABCG2 expression as mechanistic predictors of benefit, providing an updated framework for selecting patients most likely to respond to TOP1 poison/PARPi combinations and challenging the long-standing assumption that PARP inhibition can universally resensitize tumors to TOP1 poisons. Citation Format: Annapoorna Venkatachalam, Caroline C. Nitschmann, Karen S. Flatten, Rachel Hurley, Xiaonan Hou, Cristina Correia, Paula A. Schenider, Krista Goergen, Matthew Maurer, Ethan P. Heinzen, Ann L. Oberg, S John Weroha, Paul Haluska, Scott H. Kaufmann, Elizabeth M. Swisher. TOP1-DNA protein complex as a predictive biomarker for NKTR-102/Rucaparib combination therapy in PARP-inhibitor resistant ovarian cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(8_Suppl):Abstract nr LB364.

Abstract Background Interpretation of multi-analyte laboratory profiles, such as lupus anticoagulant (LAC) testing, requires integration of numerous test results and clinical information. However, this process is time-consuming, requires domain knowledge, and has limited scalability. Given that rule-based systems are rigid and difficult to maintain and large language models pose risks of “hallucinations,” we developed and evaluated a novel retrieval-based approach to automate LAC profile interpretation to improve efficiency while maintaining fidelity and reliability for clinical use. Methods Our method processes laboratory values through ensemble random forest (RF) models to retrieve the most similar expert interpretation from a curated database of 347 previously interpreted LAC profiles. The system's performance was assessed through technical accuracy metrics, a work flow efficiency timing study, and expert review of interpretation accuracy for held-out cases. Results Technical validation demonstrated high accuracy in retrieving correct interpretative elements (median: 93% accuracy, interquartile range [IQR]: 67% to 100%). Retrieval accuracy was highly correlated with the prevalence of a given element (ρ = 0.8307). In a timing study of 93 consecutive LAC profiles, ensemble-assisted interpretation achieved a median time of 22 s per report (IQR: 16 s to 31 s), representing a 78.6% total reduction compared to manual interpretation (median: 118 s, IQR: 96 s to 172 s; t = 18.61, P < 0.001). Furthermore, 92.5% of AI-assisted interpretations were completed within 60 s, exceeding the target of 85%. Expert review confirmed 100% concordance for LAC presence/absence. Conclusions The proposed approach significantly improves efficiency of complex laboratory interpretation for LAC profiles, while maintaining consistency and high fidelity. Future studies will explore the method’s inherent extensibility to the interpretation of other multi-analyte laboratory profiles.

Abstract Alzheimer's disease (AD) continues to pose a major public health challenge. Since its launch in 2004, the Alzheimer's Disease Neuroimaging Initiative (ADNI) has played a pivotal role in advancing the field by providing a comprehensive, open‐access, longitudinal dataset that integrates neuroimaging, biomarker, genetic, and clinical data related to AD. We used standard literature search methods to identify ∼1830 publications from 2023 to mid‐2025 that used ADNI data or samples. This review highlights key ADNI studies with direct clinical applications. We describe how these have impacted the development and validation of plasma biomarkers, improved clinical trials, assessed AD therapies, and developed methods for diagnosis and prediction using clinical, fluid, and imaging biomarkers. These contributions are underlain by an improved understanding of biological mechanisms of disease progression in AD and highlight ADNI's central role in advancing translational research and accelerating progress toward more effective, individualized care for patients with AD.

ABSTRACT Background Visceral myopathy (VSCM) is an ultra‐rare life‐threatening condition characterized by severe impairment of gastrointestinal (GI), genitourinary, and uterine smooth muscle. This disorder represents a significant clinical challenge due to variable presentation and the lack of standardized diagnostic and therapeutic protocols. Methods To discuss advances in the field, scientists and clinicians with a special interest in VSCM met in Arenzano, Genova, Italy in October 2024 for the second International Forum on Visceral Myopathy 2024 (IFVM2024) ( https://ifvm2024.ge.ibf.cnr.it/ ). Key Results As in the previous edition of the event ( https://poic‐e‐dintorni.org/efvm‐2022/ ), attendees included clinicians and researchers from around the world who study this disease, representatives from support organizations, patients affected by VSCM and their families, and companies that co‐funded the event. The present manuscript aims to summarize knowledge shared during the IFVM2024 conference, thus providing an updated state‐of‐the‐art summary of VSCM biology and disease management. Conclusions Here, we pay particular attention to the epidemiology of the disease, histopathology, genetics, novel treatments, advances in molecular and cell biology, experimental models, and the lived experiences and impact of this disorder on families.

Abstract Background Anti‐M antibodies were described as rarely eliciting hemolytic disease of the fetus and newborn (HDFN) in pregnancy. More recent literature, primarily from Asia, reports severe HDFN attributed to maternal anti‐M. The objective of this study is to determine if there is a difference in the frequency and/or severity of anti‐M HDFN among subjects who self‐report as Asian compared to non‐Asian. Methods Retrospective data were collected from 18 sites. Fetuses/neonates affected by HDFN were categorized as unaffected, mild, moderate, or severe based on clinical management and outcomes. Results Anti‐M was identified in 557 obstetric patients. Among 332 neonates with complete data for analysis, anti‐M was detected in isolation during the pregnancies of 210 (63.3%) neonates; 198 (94.3%) were unaffected by HDFN. The overall frequency of HDFN was 6.5% (2/31) in Asian patients and 5.6% (10/179) in non‐Asian patients. The frequency of severe HDFN was 6.5% (2/31) among Asian patients and 0.6% (1/179) among non‐Asian patients. Among non‐Asian patients, eight (4.5%) were mild and one (0.6%) was moderate. Of 63 pregnancies with anti‐M and ABO incompatibility, 6 (9.5%) were associated with mild HDFN and 57 (90.5%) were unaffected. Of 49 pregnancies with anti‐M and non‐ABO antibody(s), 6 (12.2%) were associated with mild HDFN and 43 (87.8%) were unaffected. One case (10.0%) of HDFN (mild) occurred in the presence of anti‐M, additional antibody(s), and ABO incompatibility. Conclusion In this study, severe anti‐M HDFN was rare in non‐Asian subjects. There is a signal that Asian patients are at greater risk for severe anti‐M HDFN.

Background Immune exclusion inhibits antitumor immunity and response to immunotherapy, but its mechanisms remain poorly defined. In triple-negative breast cancer (TNBC), an aggressive and generally immune-rich subtype, an immune-cold microenvironment predicts poor prognosis due to a limited response to chemotherapy and immune checkpoint inhibitors. This study aimed to identify mechanisms regulating immune infiltration in TNBC. Methods We performed spatial transcriptomic analysis comparing immune-enriched versus immune-cold treatment-naïve TNBCs. Functional analyses, including loss-of-function and reconstitution experiments, were conducted to investigate the role of trophoblast cell-surface antigen 2 (TROP2), a key target of anticancer antibody drug conjugates (ADCs), in promoting TNBC progression. A humanized TROP2 syngeneic TNBC model was used to assess the effects of TROP2-targeting in combination with anti-programmed cell death protein 1 (PD-1) therapy. Additionally, data from patients treated with immune checkpoint blockade were used to test hypotheses from the preclinical findings. Results We reveal that TROP2 controls barrier-mediated immune exclusion in TNBC through claudin 7 association and tight junction regulation. TROP2 expression is inversely correlated with T-cell infiltration and predicts poor outcomes in TNBC. We demonstrate that TROP2 is sufficient to drive tumor progression in vivo in a CD8 T cell-dependent manner, while its loss deregulates expression and localization of multiple tight junction proteins, enabling T-cell infiltration. We show that TROP2 targeting via hRS7, the antibody component of the ADC sacituzumab govitecan, enhances the anti-PD-1 response and improves T-cell accessibility and effector function. Correspondingly, TROP2 expression is highly associated with lack of response to anti-PD-1 therapy in human breast cancer. Conclusions This study defines a new mechanism of barrier-mediated immune exclusion in cancer controlled by TROP2-dependent tight junctions. This mechanism drives tumor progression but can be targeted via TROP2-directed therapy to activate antitumor immunity and enhance immunotherapy response.

Abstract Large Language Models (LLM) are being widely adopted into the medical field for their impressive ability to analyze and summarize large amounts of text data. These models enable clinicians and researchers to extract meaningful insights from complex datasets and may assist with decision making. Here we present our workflows and application of LLM for the interpretation and summarization of clinical data related to CAR-T cell therapy. Using an LLM (Gemini 2.5 pro) in the Google Cloud Computing (GCP) environment, two applications were developed to analyze CAR-T cell therapy clinical data: 1) extracting and summarizing CRS and ICANS event-related data to streamline the compliance team workflow, and 2) identifying features available at time of CAR-T infusion able to classify patients into high- or low-monitoring needs 14 days post CAR-T. Patient data (vitals, labs, hematology notes, and EKGs) were extracted from the electronic medical record (EMR) using Google BigQuery into SQL tables in GCP. For each application, relevant data fields were retrieved, formatted into JSON objects, and embedded in the LLM prompt for context-aware processing. Both applications have undergone iterative prompt engineering after analyzing the LLM output against ground truth data in the EMR. For the application extracting and summarizing CRS and ICANS events, the LLM was optimized on Mayo Clinic Rochester data. When compared to the IEC compliance database, the LLM achieved 100% accuracy and F1 score for CRS events, and 96% accuracy and 82% F1 score for ICANS events. The match rate for CRS and ICANS grades were 83% and 89% respectively. We applied the same LLM to Mayo Clinic Arizona (MCA) and Mayo Clinic Florida (MCF) cases, who document clinical notes and toxicity flowsheet differently, and achieved an accuracy (MCA: 93%, MCF:93%) and F1 score (MCA: 96%, MCF: 96%) for CRS and accuracy (MCA: 81%, MCF:82%) and F1 score (MCA: 76%, MCF: 75%) for ICANS. LLM was able to capture events missed by manual reviews. For most of the discrepancies where compliance team final adjudication is needed, LLM will be updated to flag discrepancies for review by the compliance team. For the application related to the monitoring needs 14 days post CAR-T, the LLM identified 5 categories predictive of high or low monitoring needs post CAR-T infusion (disease status, inflammatory and tumor burden markers, hematologic status, renal function and performance status). Our model achieved a sensitivity of 85.7%, specificity of 23.8%, and F1 score of 65.5% for our first cohort, compared with data extracted by the LLM from the EMR. For our second cohort, with demographics statistically similar to cohort 1 and using the same 5 categories, the LLM achieved a sensitivity of 83.3%, a specificity of 28.6%, and F1 score of 65.4%. Our workflow and applications of LLM’s provide examples and guidance to others interested in applying LLM’s for clinical and research applications. Citation Format: Emmanuel Contreras Guzman, Matthew Jankowski, Andre De Menezes Silva Corraes, Malvika Gupta, Monica L. Shaw, Madiha Iqbal, Talal Hilal, Saurabh Chhabra, Ricardo Daniel Parrondo, Jody K. Mclean, Kim R. Riester, Kayla Joseph, Melinda Tan, Holly Ross, Cleyonia Barnett, Sylvia Carter, Semy Girmay, Rachel Wolan, Milana Ramsey, Christian Downhour, Kristy Morgan, Shae Sibley, Erica Rushing, Lucy Holmes, Allison Burgstahler, Stephen M. Ansell, Hassan Alkhateeb, Matthew Hathcock, Ramona Bruno, Allison C. Rosenthal, Hemant Murthy, Patrick B. Johnston, Jonas Paludo, Yi Lin. Applications of large language models to CAR-T cell therapy clinical data using Google cloud computing [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 2740.