Hasil untuk "Neoplasms. Tumors. Oncology. Including cancer and carcinogens"

Jie He, Nan Liu, Lin Li et al.

Abstract Objectives This study aimed to assess the prognostic significance of CD40LG and a related radiomics model in high-grade gliomas. Methods This retrospective cohort study utilized data from TCGA (n = 298) and TCIA (n = 89) following STROBE guidelines. From The Cancer Genome Atlas (TCGA), HGGs with genomic and clinical data were analyzed to establish CD40LG's prognostic value through Kaplan-Meier survival analysis and multivariate Cox regression. A radiomic model, based on TCGA data and matched MRI T1 images from The Cancer Imaging Archive (TCIA), was built to predict CD40LG levels. Radiomic features were extracted via PyRadiomics, filtered by 1000-repeat LASSO regression, and validated through 5-fold cross-validation. An independent cohort (n = 182) tested the model's prognostic utility. Subsequently, a prognostic model and nomogram were developed. Results Kaplan–Meier curves indicated a significant association between CD40LG expression and overall survival. CD40LG emerged as a crucial risk factor in both univariate and multivariate analyses. Immune cell infiltration analyses highlighted CD40LG's connection to the tumor immune microenvironment. A radiomic model, constructed using LASSO regression and five features, successfully predicted CD40LG expression pre-surgery. Combining the model's Rad-scores with clinical data, we created an effective prognostic model. Conclusions CD40LG expression correlates with high-grade glioma prognosis. Our MRI-based radiomic signature predicted CD40LG expression and prognosis, offering potential guidance for treatment decisions and future research.

Soroosh Sabeti, Nicholas B. Larson, Judy C. Boughey et al.

Abstract Background Angiogenic activity of cancerous breast tumors can be impacted by neoadjuvant chemotherapy (NAC), thus potentially serving as a marker for response monitoring. While different imaging modalities can aid in evaluation of tumoral vascular changes, ultrasound-based approaches are particularly suitable for clinical use due to their availability and noninvasiveness. In this paper, we make use of quantitative high-definition microvasculature imaging (qHDMI) based on contrast-free ultrasound for assessment of NAC response in breast cancer patients. Methods Patients with invasive breast cancer recommended treatment with NAC were included in the study and ultrafast ultrasound data were acquired at pre-NAC, mid-NAC, and post-NAC time points. Data acquisitions also took place at two additional timepoints – at two and four weeks after NAC initiation in a subset of patients. Ultrasound data frames were processed within the qHDMI framework to visualize the microvasculature in and around the breast tumors. Morphological analyses on the microvasculature structure were performed to obtain 12 qHDMI biomarkers. Pathology from surgery classified response using residual cancer burden (RCB) and was used to designate patients as responders (RCB 0/I) and non-responders (RCB II/III). Distributions of imaging biomarkers across the two groups were analyzed using Wilcoxon rank-sum test. The trajectories of biomarker values over time were investigated and linear mixed effects models were used to evaluate interactions between time and group for each biomarker. Results Of the 53 patients included in the study, 32 (60%) were responders based on their RCB status. The results of linear mixed effects model analysis showed statistically significant interactions between group and time in six out of the 12 qHDMI biomarkers, indicating differences in trends of microvascular morphological features by responder status. In particular, vessel density (p-value: 0.023), maximum tortuosity (p-value: 0.049), maximum diameter (p-value: 0.002), fractal dimension (p-value: 0.002), mean Murray’s deviation (p-value: 0.034), and maximum Murray’s deviation (p-value: 0.022) exhibited significantly different trends based on responder status. Conclusions We observed microvasculature changes in response to NAC in breast cancer patients using qHDMI as an objective and quantitative contrast-free ultrasound framework. These finding suggest qHDMI may be effective in identifying early response to NAC.

Sujuan Dai, Lin Li, Guangxiu Guo et al.

Abstract Triple-negative breast cancer (TNBC) is an aggressive subtype lacking targeted therapies. In this study, we aimed to investigate the pivotal role of cyclin E1 (CCNE1) in the onset and progression of TNBC using comprehensive bioinformatic analysis and functional validation. We found significantly elevated CCNE1 expression in TNBC tissues compared to normal, which correlated with poor prognosis. Functional assessments in vitro and in vivo demonstrated that knockdown of CCNE1 impaired the proliferative, migratory, and invasive capacities of TNBC cells, promoted apoptosis, and reduced tumorigenicity. Furthermore, CCNE1 sustains the stem-like properties of TNBC cells and fuels malignant progression through Anillin (ANLN). Mechanistically, CCNE1 interacted with ANLN and stabilized its protein levels by counteracting Fizzy-related protein 1 (FZR1)-mediated the ubiquitination modification in TNBC. Mutation of the ubiquitination site in ANLN affected CCNE1’s regulatory functions but not ANLN’s intrinsic properties. Taken together, these findings underscore the role of CCNE1 in promoting TNBC cell stemness and progression via competitive inhibition of FZR1-mediated ANLN ubiquitination. Consequently, targeting CCNE1 emerges as a promising therapeutic approach for breast cancer.

Marianne Abémgnigni Njifon, Tobias Weber, Viktor Bezborodov et al.

Accurate tumor classification is essential for selecting effective treatments, but current methods have limitations. Standard tumor grading, which categorizes tumors based on cell differentiation, is not recommended as a stand-alone procedure, as some well-differentiated tumors can be malignant. Tumor heterogeneity assessment via single-cell sequencing offers profound insights but can be costly and may still require significant manual intervention. Many existing statistical machine learning methods for tumor data still require complex pre-processing of MRI and histopathological data. In this paper, we propose to build on a mathematical model that simulates tumor evolution (Ożański (2017)) and generate artificial datasets for tumor classification. Tumor heterogeneity is estimated using normalized entropy, with a threshold to classify tumors as having high or low heterogeneity. Our contributions are threefold: (1) the cut and graph generation processes from the artificial data, (2) the design of tumor features, and (3) the construction of Block Graph Neural Networks (BGNN), a Graph Neural Network-based approach to predict tumor heterogeneity. The experimental results reveal that the combination of the proposed features and models yields excellent results on artificially generated data ($89.67\%$ accuracy on the test data). In particular, in alignment with the emerging trends in AI-assisted grading and spatial transcriptomics, our results suggest that enriching traditional grading methods with birth (e.g., Ki-67 proliferation index) and death markers can improve heterogeneity prediction and enhance tumor classification.

Chrystelle Kiang, Micah Streiff, Rebecca Nash et al.

Although the descriptive epidemiology of primary breast cancer is well characterized in the US, breast cancer recurrence rates have not been measured in an unselected population. The number of breast cancer survivors at risk for recurrence is growing each year, so recurrence surveillance is a pressing need. We used missing data methods to impute breast cancer recurrence and estimate the risk of recurrence in the Cancer Recurrence Information and Surveillance Program (CRISP) cohort in the Georgia Cancer Registry. The imputation model was based on an internal validation substudy and indicators recorded in the registry (e.g., pathology reports, imaging claims), prognostic variables (e.g., stage at diagnosis), and characteristics associated with missing data (e.g., insurance coverage). We pooled hazard ratios (HR) and 95% Confidence Intervals (CI) across 1000 imputed datasets, adjusted for age, stage, grade, subtype, race and ethnicity, marital status, and urban/rural county at diagnosis. There were 1,606 patients with a validated outcome (75% with breast cancer recurrence) and we imputed the outcome for the remaining 23,439 patients. We estimated an overall 7.2% incidence of recurrence between at least 1 year after diagnosis and up to 5 years of follow up. When comparing the hazards pooled across imputations, we found that some patterns differed from established patterns in mortality or survival, notably by race and ethnicity, underscoring the need for continued research on the descriptive epidemiology of breast cancer recurrence. These results provide new insights into surveillance for breast cancer survivors in Georgia, especially those with higher stage and grade tumors, of Hispanic ethnicity, and who may be lacking social support.

Ahmed M. Taha, Salah A. Aly, Mohamed F. Darwish

Accurate and quick diagnosis of normal brain tissue Glioma, Meningioma, and Pituitary Tumors is crucial for optimal treatment planning and improved medical results. Magnetic Resonance Imaging (MRI) is widely used as a non-invasive diagnostic tool for detecting brain abnormalities, including tumors. However, manual interpretation of MRI scans is often time-consuming, prone to human error, and dependent on highly specialized expertise. This paper proposes an advanced AI-driven technique to detecting glioma, meningioma, and pituitary brain tumors using YoloV11 and YoloV8 deep learning models. Methods: Using a transfer learning-based fine-tuning approach, we integrate cutting-edge deep learning techniques with medical imaging to classify brain tumors into four categories: No-Tumor, Glioma, Meningioma, and Pituitary Tumors. Results: The study utilizes the publicly accessible CE-MRI Figshare dataset and involves fine-tuning pre-trained models YoloV8 and YoloV11 of 99.49% and 99.56% accuracies; and customized CNN accuracy of 96.98%. The results validate the potential of CNNs in achieving high precision in brain tumor detection and classification, highlighting their transformative role in medical imaging and diagnostics.

Hongyi Pan, Gorkem Durak, Elif Keles et al.

Pancreatic cancer is projected to be the second-deadliest cancer by 2030, making early detection critical. Intraductal papillary mucinous neoplasms (IPMNs), key cancer precursors, present a clinical dilemma, as current guidelines struggle to stratify malignancy risk, leading to unnecessary surgeries or missed diagnoses. Here, we developed Cyst-X, an AI framework for IPMN risk prediction trained on a unique, multi-center dataset of 1,461 MRI scans from 764 patients. Cyst-X achieves significantly higher accuracy (AUC = 0.82) than both the established Kyoto guidelines (AUC = 0.75) and expert radiologists, particularly in correct identification of high-risk lesions. Clinically, this translates to a 20% increase in cancer detection sensitivity (87.8% vs. 64.1%) for high-risk lesions. We demonstrate that this performance is maintained in a federated learning setting, allowing for collaborative model training without compromising patient privacy. To accelerate research in early pancreatic cancer detection, we publicly release the Cyst-X dataset and models, providing the first large-scale, multi-center MRI resource for pancreatic cyst analysis.

Yipeng Zhuang, Yifeng Guo, Yuewen Li et al.

Lung cancer patients frequently experience breakthrough pain episodes, with up to 91% requiring timely intervention. To enable proactive pain management, we propose a hybrid machine learning and large language model pipeline that predicts pain episodes within 48 and 72 hours of hospitalization using both structured and unstructured electronic health record data. A retrospective cohort of 266 inpatients was analyzed, with features including demographics, tumor stage, vital signs, and WHO-tiered analgesic use. The machine learning module captured temporal medication trends, while the large language model interpreted ambiguous dosing records and free-text clinical notes. Integrating these modalities improved sensitivity and interpretability. Our framework achieved an accuracy of 0.874 (48h) and 0.917 (72h), with an improvement in sensitivity of 8.6% and 10.4% due to the augmentation of large language model. This hybrid approach offers a clinically interpretable and scalable tool for early pain episode forecasting, with potential to enhance treatment precision and optimize resource allocation in oncology care.

Yue Zhang, Jiahua Dong, Chengtao Peng et al.

Accurate segmentation of breast tumors in magnetic resonance images (MRI) is essential for breast cancer diagnosis, yet existing methods face challenges in capturing irregular tumor shapes and effectively integrating local and global features. To address these limitations, we propose an uncertainty-gated deformable network to leverage the complementary information from CNN and Transformers. Specifically, we incorporates deformable feature modeling into both convolution and attention modules, enabling adaptive receptive fields for irregular tumor contours. We also design an Uncertainty-Gated Enhancing Module (U-GEM) to selectively exchange complementary features between CNN and Transformer based on pixel-wise uncertainty, enhancing both local and global representations. Additionally, a Boundary-sensitive Deep Supervision Loss is introduced to further improve tumor boundary delineation. Comprehensive experiments on two clinical breast MRI datasets demonstrate that our method achieves superior segmentation performance compared with state-of-the-art methods, highlighting its clinical potential for accurate breast tumor delineation.

Xiaofei Wang, Hanyu Liu, Yupei Zhang et al.

Cancers are characterized by remarkable heterogeneity and diverse prognosis. Accurate cancer classification is essential for patient stratification and clinical decision-making. Although digital pathology has been advancing cancer diagnosis and prognosis, the paradigm in cancer pathology has shifted from purely relying on histology features to incorporating molecular markers. There is an urgent need for digital pathology methods to meet the needs of the new paradigm. We introduce a novel digital pathology approach to jointly predict molecular markers and histology features and model their interactions for cancer classification. Firstly, to mitigate the challenge of cross-magnification information propagation, we propose a multi-scale disentangling module, enabling the extraction of multi-scale features from high-magnification (cellular-level) to low-magnification (tissue-level) whole slide images. Further, based on the multi-scale features, we propose an attention-based hierarchical multi-task multi-instance learning framework to simultaneously predict histology and molecular markers. Moreover, we propose a co-occurrence probability-based label correlation graph network to model the co-occurrence of molecular markers. Lastly, we design a cross-modal interaction module with the dynamic confidence constrain loss and a cross-modal gradient modulation strategy, to model the interactions of histology and molecular markers. Our experiments demonstrate that our method outperforms other state-of-the-art methods in classifying glioma, histology features and molecular markers. Our method promises to promote precise oncology with the potential to advance biomedical research and clinical applications. The code is available at https://github.com/LHY1007/M3C2

Zegang Ruan, Xin Zhuo, Chenyang Xu

With the increasing implementation of early lung cancer screening and the increasing emphasis on physical examinations, the early-stage lung cancer detection rate continues to rise. Visceral pleural invasion (VPI), which denotes the tumor’s breach of the elastic layer or reaching the surface of the visceral pleura, stands as a pivotal factor that impacts the prognosis of patients with non-small cell lung cancer (NSCLC) and directly influences the pathological staging of early-stage cases. According to the latest 9th edition of the TNM staging system for NSCLC, even when the tumor diameter is less than 3 cm, the final T stage remains T2a if VPI is present. There is considerable controversy within the guidelines regarding treatment options for stage IB NSCLC, especially among patients exhibiting VPI. Moreover, the precise determination of VPI is important in guiding treatment selection and prognostic evaluation in individuals with NSCLC. This article aims to provide a comprehensive review of the current status and advancements in studies pertaining to stage IB NSCLC accompanied by VPI.

Jiaying Tao, Zhixian Shi, Mulan Li et al.

Growing teratoma syndrome (GTS) is a rare condition that arises secondary to malignant germ cell tumors. It is characterized by an enlarging abdominal mass during or after chemotherapy, normal tumor markers, and histopathological indications of mature teratoma components. Awareness of GTS is limited, and it is often mistaken for disease progression or recurrence. This misdiagnosis can lead to delayed treatment and increased risk of complications. Therefore, early identification of GTS is crucial to avoid unnecessary systemic treatments and reduce financial burden. GTS is unresponsive to chemotherapy or radiotherapy and complete surgical resection is the sole therapeutic strategy. In this report, we present a case of GTS in a 20-year-old female following treatment for immature teratoma, alongside a review of the relevant literature aimed at enriching our insight into the clinical manifestations of GTS.

Davide Parodi, Edoardo Dighero, Giorgia Biddau et al.

Purpose: Analysis of [18F]-Fluorodeoxyglucose (FDG) kinetics in cancer has been most often limited to the evaluation of the average uptake over relatively large volumes. Nevertheless, tumor lesion almost contains inflammatory infiltrates whose cells are characterized by a significant radioactivity washout due to the hydrolysis of FDG-6P catalyzed by glucose-6P phosphatase. The present study aimed to verify whether voxel-wise compartmental analysis of dynamic imaging can identify tumor regions characterized by tracer washout. Materials & Methods: The study included 11 patients with lung cancer submitted to PET/CT imaging for staging purposes. Tumor was defined by drawing a volume of interest loosely surrounding the lesion and considering all inside voxels with standardized uptake value (SUV) >40% of the maximum. After 20 minutes dynamic imaging centered on the heart, eight whole body scans were repeated. Six parametric maps were progressively generated by computing six regression lines that considered all eight frames, the last seven ones, and so on, up to the last three. Results: Progressively delaying the starting point of regression line computation identified a progressive increase in the prevalence of voxels with a negative slope. Conclusions: The voxel-wise parametric maps provided by compartmental analysis permits to identify a measurable volume characterized by radioactivity washout. The spatial localization of this pattern is compatible with the recognized preferential site of inflammatory infiltrates populating the tumor stroma and might improve the power of FDG imaging in monitoring the effectiveness of treatments aimed to empower the host immune response against the cancer.

Kyrie Zhixuan Zhou, Royta Iftakher, Sean P. Mullen et al.

Cancer survivors experience a wide range of impairments arising from cancer or its treatment, such as chemo brain, visual impairments, and physical impairments. These impairments degrade their quality of life and potentially make software use more challenging for them. However, there has been limited research on designing accessible software for cancer survivors. To bridge this research gap, we conducted a formative study including a survey (n=46), semi-structured interviews (n=20), and a diary study (n=10) with cancer survivors. Our results revealed a wide range of impairments experienced by cancer survivors, including chemo brain, neuropathy, and visual impairments. Cancer survivors heavily relied on software for socialization, health purposes, and cancer advocacy, but their impairments made software use more challenging for them. Based on the results, we offer a set of accessibility guidelines that software designers can utilize when creating applications for cancer survivors. Further, we suggest design features for inclusion, such as health resources, socialization tools, and games, tailored to the needs of cancer survivors. This research aims to spotlight cancer survivors' software accessibility challenges and software needs and invite more research in this important yet under-investigated domain.

S. Shabir, A. Asiaf

Objective: Ovarian cancer is the most dreadful gynecological malignancy among females worldwide, with worst prognosis and non-effectiveness of its screening markers yielding false negative cases. The aim of the present study was to investigate the clinicopathological and epidemiological profile of ovarian cancer patients in Kashmir ethnicity. Patients and Methods: The present observational cross-sectional study was conducted on 50 ovarian cancer patients who reported to the Department of General Surgery and Medical Oncology, SKIMS, Srinagar, from 2017 to 2019 and on 50 healthy female volunteers as age-matched controls. The clinicopathological and epidemiological profiles of the ovarian cancer patients were compared with those of normal controls. A detailed description of clinico-pathological, epidemiological, and etiological data was obtained from the in-patient record and questionnaire method and analyzed by Student's t-test to estimate statistically significant differences between cases and controls. The blood samples were assessed for CA-125 levels. p ≥ 0.05 differences were considered statistically significant. Results: Of the 50 ovarian cancer patients, the most affected (48%) age group was 44–59 years. The majority (64%) of the patients belonged to stages III and IV of the disease. Abdominal distension and pelvic pain were the most frequent symptoms observed in 46% and 44% of cases, respectively. Using Pearson’s correlation coefficient, we observed a significant negative correlation of risk of ovarian cancer with menarcheal age and a highly significant positive correlation with menopausal age, age at marriage, BMI, and CA125 in ovarian cancer patients. Conclusions: Most of the patients presented in an advanced stage of the disease and had CA125 levels of 500 U/ml. Awareness must be raised among women regarding the symptoms, warning signs, and risk factors of ovarian cancer because doing so will facilitate the early diagnosis of the disease.

Xiangyi Kong, Peng Gao, Jing Wang et al.

Abstract Early detection and diagnosis of many cancers is very challenging. Late stage detection of a cancer always leads to high mortality rates. It is imperative to develop novel and more sensitive and effective diagnosis and therapeutic methods for cancer treatments. The development of new cancer treatments has become a crucial aspect of medical advancements. Nanobots, as one of the most promising applications of nanomedicines, are at the forefront of multidisciplinary research. With the progress of nanotechnology, nanobots enable the assembly and deployment of functional molecular/nanosized machines and are increasingly being utilized in cancer diagnosis and therapeutic treatment. In recent years, various practical applications of nanobots for cancer treatments have transitioned from theory to practice, from in vitro experiments to in vivo applications. In this paper, we review and analyze the recent advancements of nanobots in cancer treatments, with a particular emphasis on their key fundamental features and their applications in drug delivery, tumor sensing and diagnosis, targeted therapy, minimally invasive surgery, and other comprehensive treatments. At the same time, we discuss the challenges and the potential research opportunities for nanobots in revolutionizing cancer treatments. In the future, medical nanobots are expected to become more sophisticated and capable of performing multiple medical functions and tasks, ultimately becoming true nanosubmarines in the bloodstream. Graphical abstract

Jieneng Chen, Yingda Xia, Jiawen Yao et al.

Human readers or radiologists routinely perform full-body multi-organ multi-disease detection and diagnosis in clinical practice, while most medical AI systems are built to focus on single organs with a narrow list of a few diseases. This might severely limit AI's clinical adoption. A certain number of AI models need to be assembled non-trivially to match the diagnostic process of a human reading a CT scan. In this paper, we construct a Unified Tumor Transformer (CancerUniT) model to jointly detect tumor existence & location and diagnose tumor characteristics for eight major cancers in CT scans. CancerUniT is a query-based Mask Transformer model with the output of multi-tumor prediction. We decouple the object queries into organ queries, tumor detection queries and tumor diagnosis queries, and further establish hierarchical relationships among the three groups. This clinically-inspired architecture effectively assists inter- and intra-organ representation learning of tumors and facilitates the resolution of these complex, anatomically related multi-organ cancer image reading tasks. CancerUniT is trained end-to-end using a curated large-scale CT images of 10,042 patients including eight major types of cancers and occurring non-cancer tumors (all are pathology-confirmed with 3D tumor masks annotated by radiologists). On the test set of 631 patients, CancerUniT has demonstrated strong performance under a set of clinically relevant evaluation metrics, substantially outperforming both multi-disease methods and an assembly of eight single-organ expert models in tumor detection, segmentation, and diagnosis. This moves one step closer towards a universal high performance cancer screening tool.

Khan Hina, Jafry Rabab, Rios Adan et al.

LLM Abstracts Supplement P-001 | A Case of Acute Undifferentiated Leukemia: Diagnostic and Therapeutic Challenges Khan Hina, Jafry Rabab, Rios Adan Department of Hematology/Oncology, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, Texas, USA; 2 Aga Khan Medical University, Karachi, Pakistan Introduction/Background/Significance: Acute undifferentiated leukemia (AUL) is a rare, aggressive leukemia lacking expression of lineage-specific markers. Diagnostic and therapeutic challenges include differentiating AUL from acute leukemias of ambiguous lineage (ALALs). The diagnosis of AUL is made once comprehensive immunophenotyping, cytogenetics, and molecular genomics exclude any lineage-specific leukemia. Materials and Methods/Case Presentation/Objective: A 56-year-old woman presented with easy bruising, petechial rash, and fevers for 2 weeks. Laboratory tests revealed a hemoglobin of 8.5 g/dL, WBC 8.1 K WBC/μL, and platelets of 5,000 platelets/μL. Serum LDH was mildly elevated at 288 U/L. Peripheral blood smear showed increased blasts. Bone marrow aspirate and biopsy showed 75% blasts that expressed no specific lineage markers. Cytogenetics were normal, and molecular findings revealed the presence of an IDH1 mutation. A diagnosis of AUL was confirmed. The patient was treated with FLAGIDA (fludarabine 30 mg/m x 5 days, cytarabine 1.5 g/m x 5 days, idarubicin 8 mg/m x 3 days, and G-CSF with tbo-filgastrim 480 μg from day 1 x 7 days). Her induction course was complicated by neutropenic fever and sepsis requiring intravenous antibiotics. A bone marrow from day 23 of treatment revealed a complete response with incomplete hematologic recovery. The patient was referred for allogenic hematopoietic stem cell transplantation (HSCT). Results/Description/Main Outcome Measure: ALALs are extremely rare hematologic malignancies, accounting for 1 year duration. P-002 | A First-in-Human Study of CD123 Natural Killer Cell Engager (NKCE) SAR443579 in Relapsed or Refractory Acute Myeloid Leukemia (R/R AML), B-cell Acute Lymphoblastic Leukemia (B-ALL) or High Risk-Myelodysplasia (HR-MDS) Stein Anthony, Lavrencic Mojca, Garciaz Sylvain, Huls Gerwin, Maiti Abhishek, Boissel Nicolas, Botton Stephane, Fleming Shaun, Zwaan Michel, de Leeuw David C., Desai Pinkal, Arellano Martha, Avigan David, Langemeijer Saskia, Jensen Kyle, Wagenaar Timothy, Mi Gu, Abbadessa Giovanni, Bajel Ashish City of Hope National Medical Center, Duarte, CA, USA; Erasmus University Medical Center, Rotterdam, Netherlands; Institut PaoliCalmettes, Aix-Marseille University, Marseille, France; University Medical Center Groningen, Groningen, Netherlands; MD Anderson Cancer Center, Houston, TX, USA; Hôpital Saint-Louis, Paris, France; Institut Gustave Roussy, Paris, France; Alfred Health, Melbourne, Australia; Princess Máxima Center for Pediatric Oncology, Utrecht, Netherlands; Amsterdam University Medical Center, Amsterdam, Netherlands; Weill Cornell Medicine, NY, USA; Emory University, Atlanta, GA, USA; Beth Israel Deaconess Medical Center, MA, USA; Radboud University Medical Center, Nijmegen, Netherlands; Sanofi, Cambridge, MA, USA; Peter MacCallum Cancer Centre and Royal Melbourne Hospital, Melbourne, Australia Introduction/Background/Significance: SAR443579, a trifunctional antiCD123 NKp6/CD16 NKCE, enables a cytolytic synapse between NK cells and CD123+ tumor cells resulting in NK cell activation and tumor cell killing. Here we report preliminary safety, efficacy, and PK/PD data of SAR443579 from a phase 1/2 trial in patients with R/R AML, B-ALL or HR-MDS (NCT05086315). Accepted: 19 September 2023 DOI: 10.1002/ajh.27112 Am J Hematol. 2023;98:S3–S57. wileyonlinelibrary.com/journal/ajh © 2023 Wiley Periodicals LLC. S3 Materials and Methods/Case Presentation/Objective: Patients received SAR443579 intravenously twice weekly or once weekly (QW), dependent on dose level (DL) in first 2 weeks of cycle 1, and QW for rest of the induction cycles. Patients receive approximately three 28-day induction cycles and, upon achieving a complete remission (CR) or incomplete hematologic recovery (CRi) per International Working Group criteria, may transition to a 56-day maintenance period with dosing approximately every 29 days if not a candidate for stem cell transplantation. Peripheral blood (to determine plasma concentrations and immunogenicity) and bone marrow samples were collected for PK/PD analysis during each induction cycle. Cytokines analysis, including IL-6, INFα and IFNγ, as potential safety and PD markers, was performed. Primary objectives were safety/tolerability and antileukemic activity (composite complete remission [CRc]=CR+CRi). Results/Description/Main Outcome Measures: As of April 26, 2023, 23 patients (median age: 70 years) with R/R AML across 6 DLs (3 patients in DL1 and 4 each in DL2-DL6) were included in the safety population. Nine patients (39.1%) reported prior hematopoietic stem cell transplantation and 18 (78.3%) had prior exposure to venetoclax. Patients received escalating doses between 10-3000μg/kg/dose in cycle 1 and 100-3000μg/kg QW for other induction cycles. Dose limiting toxicities (DLTs) were not observed in 21 DLT-evaluable patients, including the highest dose of 3000μg/kg QW. CR/CRi was achieved in 3 of 16 (18.8%) patients treated at a maximal target dose of ≥1000 μg/kg/infusion. Treatment emergent adverse events (TEAEs) were reported in 22 (95.7%) patients, with grade ≥3 TEAEs in 18 (78.3%) and treatment related adverse events (TRAEs) in 16 patients (69.6%), respectively. The most common TRAE was infusion-related reactions (n=13 [56.5%]). Decreased appetite, headache, diarrhea, and nausea were reported in 2 (8.7%) patients, respectively. There was 1 case of cytokine release syndrome (grade 1) and no case of immune effector cell-associated neurotoxicity syndrome. CRc rate was 13.0% (3/23 patients evaluable for response). The median duration of CR/CRi is not estimable. From Dec 14, 2021 to Oct 17, 2022, 19 patients were treated across 5 DLs (3 in DL1 and 4 each in DL2 to DL5). In these patients, early immunogenicity was 26% with no apparent impact on safety/efficacy to date. Post first administration of SAR443579, circulating maximum concentration increased with dose increase with a supra-dose proportionality between DL1 and DL5. PK linearity was achieved at 3000μg/kg QW (DL5) at the end of cycle 1. CRS levels of IL-6 ( >500/1000pg/mL) were measured in a single DL1 patient. Conclusions: SAR443579 was well tolerated up to doses of 3000μg/ kg QW with observed clinical benefit in patients with R/R AML. The trial continues to accrue patients. P-003 | A Fulminant Leukemic Presentation of Blastic Plasmacytoid Dendritic Cell Neoplasm (BPDCN) Responsive to Tagraxufusp-erzs Lewis Toni-Ann, Amin Adina, Ghalehsari Nima, Grutman Gennadiy, Astrow Alan B.. New YorkPresbyterian Brooklyn Methodist Hospital Introduction/Background/Significance: Blastic plasmacytoid dendritic cell neoplasm (BPDCN) is a rare disease accounting for less than 1% of all hematologic malignancies. The mainstay of treatment is allogenic stem cell transplantation (allo-SCT) in consolidation of previous response. We report a case of rapidly progressive BPDCN with marrow failure and peripheral blood involvement responsive to Tagraxofusp-erzs, a CD123-targeted monoclonal antibody conjugated to diphtheria toxin approved by the Food and Drug Administration (FDA) in 2019. Materials and Methods/Case Presentation/Objective: A 71-year-old female with chronic well-controlled hypertension presented to an outside hospital for chest pain radiating to the back. Differential diagnosis included aortic dissection; however, emergency imaging revealed a uterine mass with regional adenopathy, and laboratory testing revealed 45,000 platelets, 18,300 WBC, without anemia or neutropenia. She was discharged without definitive diagnosis or treatment, and percutaneous inguinal lymph node biopsy at this facility was reported as a "poorly differentiated neoplasm" not otherwise characterized. The patient then presented to us two weeks later with increasing leftsided back pain and progressive loss of performance status. Repeat laboratory testing revealed an abnormal CBC with 15,900 WB

Chuan Yan, Qiqi Yang, Zhiyuan Gong

Gajalakshmi Ramanathan, Angela G. Fleischman