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

Yue Deng, Mingjing Li, Jinzhi Lei

Understanding how microenvironmental heterogeneity influences tumor progression is essential for advancing both cancer biology and therapeutic strategies. In this study, we develop a cellular automata (CA) model to simulate tumor growth under varying microenvironmental conditions and genetic mutation rates, addressing a gap in existing studies that rarely integrate these two factors to explain tumor dynamics. The model explicitly incorporates the cellular heterogeneity of stem and non-stem cells, dynamic cell-cell interactions, and tumor-microenvironment crosstalk. Using computational simulations, we examine the synergistic effects of gene mutation rate, initial tumor burden, and microenvironmental state on tumor progression. Our results demonstrate that lowering the mutation rate significantly mitigates tumor expansion and preserves microenvironmental integrity. Interestingly, the initial tumor burden has a limited impact, whereas the initial condition of the microenvironment critically shapes tumor dynamics. A supportive microenvironment promotes proliferation and spatial invasion, while inhibitory conditions suppress tumor growth. These findings highlight the key role of microenvironmental modulation in tumor evolution and provide computational insights that may inform more effective cancer therapies.

Peter Chang, John Kairalla, Arkaprava Roy

Heterogeneous treatment effect estimation is critical in oncology, particularly in multi-arm trials with overlapping therapeutic components and long-term survivors. These shared mechanisms pose a central challenge to identifying causal effects in precision medicine. We propose a novel covariate-dependent nonparametric Bayesian multi-treatment cure survival model that jointly accounts for common structures among treatments and cure fractions. Through latent link functions, our model leverages sharing among treatments through a flexible modeling approach, enabling individualized survival inference. We adopt a Bayesian route for inference and implement an efficient MCMC algorithm for approximating the posterior. Simulation studies demonstrate the method's robustness and superiority in various specification scenarios. Finally, application to the AALL0434 trial reveals clinically meaningful differences in survival across methotrexate-based regimens and their associations with different covariates, underscoring its practical utility for learning treatment effects in real-world pediatric oncology data.

Kyunghyun Lee, Yong-Min Shin, Minwoo Shin et al.

Early diagnosis of breast cancer is crucial, enabling the establishment of appropriate treatment plans and markedly enhancing patient prognosis. While direct magnetic resonance imaging-guided biopsy demonstrates promising performance in detecting cancer lesions, its practical application is limited by prolonged procedure times and high costs. To overcome these issues, an indirect MRI-guided biopsy that allows the procedure to be performed outside of the MRI room has been proposed, but it still faces challenges in creating an accurate real-time deformable breast model. In our study, we tackled this issue by developing a graph neural network (GNN)-based model capable of accurately predicting deformed breast cancer sites in real time during biopsy procedures. An individual-specific finite element (FE) model was developed by incorporating magnetic resonance (MR) image-derived structural information of the breast and tumor to simulate deformation behaviors. A GNN model was then employed, designed to process surface displacement and distance-based graph data, enabling accurate prediction of overall tissue displacement, including the deformation of the tumor region. The model was validated using phantom and real patient datasets, achieving an accuracy within 0.2 millimeters (mm) for cancer node displacement (RMSE) and a dice similarity coefficient (DSC) of 0.977 for spatial overlap with actual cancerous regions. Additionally, the model enabled real-time inference and achieved a speed-up of over 4,000 times in computational cost compared to conventional FE simulations. The proposed deformation-aware GNN model offers a promising solution for real-time tumor displacement prediction in breast biopsy, with high accuracy and real-time capability. Its integration with clinical procedures could significantly enhance the precision and efficiency of breast cancer diagnosis.

Arman Behnam

Accurate detection and segmentation of brain tumors from magnetic resonance imaging (MRI) are essential for diagnosis, treatment planning, and clinical monitoring. While convolutional architectures such as U-Net have long been the backbone of medical image segmentation, their limited capacity to capture long-range dependencies constrains performance on complex tumor structures. Recent advances in diffusion models have demonstrated strong potential for generating high-fidelity medical images and refining segmentation boundaries. In this work, we propose VGDM: Vision-Guided Diffusion Model for Brain Tumor Detection and Segmentation framework, a transformer-driven diffusion framework for brain tumor detection and segmentation. By embedding a vision transformer at the core of the diffusion process, the model leverages global contextual reasoning together with iterative denoising to enhance both volumetric accuracy and boundary precision. The transformer backbone enables more effective modeling of spatial relationships across entire MRI volumes, while diffusion refinement mitigates voxel-level errors and recovers fine-grained tumor details. This hybrid design provides a pathway toward improved robustness and scalability in neuro-oncology, moving beyond conventional U-Net baselines. Experimental validation on MRI brain tumor datasets demonstrates consistent gains in Dice similarity and Hausdorff distance, underscoring the potential of transformer-guided diffusion models to advance the state of the art in tumor segmentation.

David Bouget, Mathilde Gajda Faanes, Asgeir Store Jakola et al.

Magnetic resonance (MR) imaging is essential for evaluating central nervous system (CNS) tumors, guiding surgical planning, treatment decisions, and assessing postoperative outcomes and complication risks. While recent work has advanced automated tumor segmentation and report generation, most efforts have focused on preoperative data, with limited attention to postoperative imaging analysis. This study introduces a comprehensive pipeline for standardized postsurtical reporting in CNS tumors. Using the Attention U-Net architecture, segmentation models were trained for the preoperative (non-enhancing) tumor core, postoperative contrast-enhancing residual tumor, and resection cavity. Additionally, MR sequence classification and tumor type identification for contrast-enhancing lesions were explored using the DenseNet architecture. The models were integrated into a reporting pipeline, following the RANO 2.0 guidelines. Training was conducted on multicentric datasets comprising 2000 to 7000 patients, using a 5-fold cross-validation. Evaluation included patient-, voxel-, and object-wise metrics, with benchmarking against the latest BraTS challenge results. The segmentation models achieved average voxel-wise Dice scores of 87%, 66%, 70%, and 77% for the tumor core, non-enhancing tumor core, contrast-enhancing residual tumor, and resection cavity, respectively. Classification models reached 99.5% balanced accuracy in MR sequence classification and 80% in tumor type classification. The pipeline presented in this study enables robust, automated segmentation, MR sequence classification, and standardized report generation aligned with RANO 2.0 guidelines, enhancing postoperative evaluation and clinical decision-making. The proposed models and methods were integrated into Raidionics, open-source software platform for CNS tumor analysis, now including a dedicated module for postsurgical analysis.

Nehad M. Ayoub, Ghaith M. Al-Taani, Amer E. Alkhalifa et al.

Purpose. Breast cancer is a heterogeneous disease. Exploring new prognostic and therapeutic targets in patients with breast cancer is essential. This study investigated the expression of MET, ESR1, and ESR2 genes and their association with clinicopathologic characteristics and clinical outcomes in patients with breast cancer. Methods. The METABRIC dataset for breast cancer was obtained from the cBioPortal public domain. Gene expression data for MET, ESR1, and ESR2, as well as the putative copy number alterations (CNAs) for MET were retrieved. Results. The MET mRNA expression levels correlated inversely with the expression levels of ESR1 and positively with the expression levels of ESR2 (r = −0.379, p<0.001 and r = 0.066, and p=0.004, respectively). The ESR1 mRNA expression was significantly different among MET CNAs groups p<0.001. Patients with high MET/ESR1 coexpression had favorable clinicopathologic tumor characteristics and prognosticators compared to low MET/ESR1 coexpression in terms of greater age at diagnosis, reduced Nottingham Prognostic Index, lower tumor grade, hormone receptor positivity, HER2-negative status, and luminal subtype p<0.001. In contrast, patients with high MET/ESR2 coexpression had unfavorable tumor features and advanced prognosticators compared to patients with low MET/ESR2 coexpression p<0.001. No significant difference in overall survival was observed based on the MET/ESR coexpression status. However, when data were stratified based on the treatment type (chemotherapy and hormonal therapy), survival was significantly different based on the coexpression status of MET/ESR. Conclusions. Findings from our study add to the growing evidence on the potential crosstalk between MET and estrogen receptors in breast cancer. The expression of the MET/ESR genes could be a novel prognosticator and calls for future studies to evaluate the impact of combinational treatment approaches with MET inhibitors and endocrine drugs in breast cancer.

Valentin Pohyer, Elizabeth Fabre, Stéphane Oudard et al.

At the hospital, the dispersion of information regarding anti-cancer treatment makes it difficult to extract. We proposed a solution capable of identifying dates, drugs and their temporal relationship within free-text oncology reports with very few manual annotations. We used pattern recognition for dates, dictionaries for drugs and transformer language models for the relationship, combined with a data augmentation strategy. Our models achieved good prediction F1-scores, reaching 0.872. The performance of models with data augmentation outperforms those of models without. By inferring such models, we can now identify and structure thousands of previously unavailable treatment events to better apprehend solutions and patient response.

Zhongzhen Huang, Yankai Jiang, Rongzhao Zhang et al.

Existing promptable segmentation methods in the medical imaging field primarily consider either textual or visual prompts to segment relevant objects, yet they often fall short when addressing anomalies in medical images, like tumors, which may vary greatly in shape, size, and appearance. Recognizing the complexity of medical scenarios and the limitations of textual or visual prompts, we propose a novel dual-prompt schema that leverages the complementary strengths of visual and textual prompts for segmenting various organs and tumors. Specifically, we introduce CAT, an innovative model that Coordinates Anatomical prompts derived from 3D cropped images with Textual prompts enriched by medical domain knowledge. The model architecture adopts a general query-based design, where prompt queries facilitate segmentation queries for mask prediction. To synergize two types of prompts within a unified framework, we implement a ShareRefiner, which refines both segmentation and prompt queries while disentangling the two types of prompts. Trained on a consortium of 10 public CT datasets, CAT demonstrates superior performance in multiple segmentation tasks. Further validation on a specialized in-house dataset reveals the remarkable capacity of segmenting tumors across multiple cancer stages. This approach confirms that coordinating multimodal prompts is a promising avenue for addressing complex scenarios in the medical domain.

Xiaoteng Cui, Jixing Zhao, Guanzhang Li et al.

Abstract Background Metabolism reprogramming plays a vital role in glioblastoma (GBM) progression and recurrence by producing enough energy for highly proliferating tumor cells. In addition, metabolic reprogramming is crucial for tumor growth and immune‐escape mechanisms. Epidermal growth factor receptor (EGFR) amplification and EGFR‐vIII mutation are often detected in GBM cells, contributing to the malignant behavior. This study aimed to investigate the functional role of the EGFR pathway on fatty acid metabolism remodeling and energy generation. Methods Clinical GBM specimens were selected for single‐cell RNA sequencing and untargeted metabolomics analysis. A metabolism‐associated RTK‐fatty acid‐gene signature was constructed and verified. MK‐2206 and MK‐803 were utilized to block the RTK pathway and mevalonate pathway induced abnormal metabolism. Energy metabolism in GBM with activated EGFR pathway was monitored. The antitumor effect of Osimertinib and Atorvastatin assisted by temozolomide (TMZ) was analyzed by an intracranial tumor model in vivo. Results GBM with high EGFR expression had characteristics of lipid remodeling and maintaining high cholesterol levels, supported by the single‐cell RNA sequencing and metabolomics of clinical GBM samples. Inhibition of the EGFR/AKT and mevalonate pathways could remodel energy metabolism by repressing the tricarboxylic acid cycle and modulating ATP production. Mechanistically, the EGFR/AKT pathway upregulated the expressions of acyl‐CoA synthetase short‐chain family member 3 (ACSS3), acyl‐CoA synthetase long‐chain family member 3 (ACSL3), and long‐chain fatty acid elongation‐related gene ELOVL fatty acid elongase 2 (ELOVL2) in an NF‐κB‐dependent manner. Moreover, inhibition of the mevalonate pathway reduced the EGFR level on the cell membranes, thereby affecting the signal transduction of the EGFR/AKT pathway. Therefore, targeting the EGFR/AKT and mevalonate pathways enhanced the antitumor effect of TMZ in GBM cells and animal models. Conclusions Our findings not only uncovered the mechanism of metabolic reprogramming in EGFR‐activated GBM but also provided a combinatorial therapeutic strategy for clinical GBM management.

Yihui Lei, Li Lin, Shuyu Cheng et al.

The posterior line treatment of unresectable advanced or metastatic gastrointestinal (GI) tumors has always been a challenging point. In particular, for patients with microsatellite stable (MSS)/mismatch repair proficient (pMMR) 0GI tumors, the difficulty of treatment is exacerbated due to their insensitivity to immune drugs. Accordingly, finding a new comprehensive therapy to improve the treatment effect is urgent. In this study, we report the treatment histories of three patients with MSS/pMMR GI tumors who achieved satisfactory effects by using a comprehensive treatment regimen of apatinib combined with camrelizumab and TAS-102 after the failure of first- or second-line regimens. The specific contents of the treatment plan were as follows: apatinib (500 mg/d) was administered orally for 10 days, followed by camrelizumab (200 mg, ivgtt, day 1, 14 days/cycle) and TAS-102 (20 mg, oral, days 1–21, 28 days/cycle). Apatinib (500 mg/d) was maintained during treatment. Subsequently, we discuss the possible mechanism of this combination and review the relevant literature, and introduce clinical trials on anti-angiogenesis therapy combined with immunotherapy.

Inye Na, Jonghun Kim, Hyunjin Park

We introduce RadiomicsFill, a synthetic tumor generator conditioned on radiomics features, enabling detailed control and individual manipulation of tumor subregions. This conditioning leverages conventional high-dimensional features of the tumor (i.e., radiomics features) and thus is biologically well-grounded. Our model combines generative adversarial networks, radiomics-feature conditioning, and multi-task learning. Through experiments with glioma patients, RadiomicsFill demonstrated its capability to generate diverse, realistic tumors and its fine-tuning ability for specific radiomics features like 'Pixel Surface' and 'Shape Sphericity'. The ability of RadiomicsFill to generate an unlimited number of realistic synthetic tumors offers notable prospects for both advancing medical imaging research and potential clinical applications.

Yao Ruan, Yao Ruan, Zhe Li et al.

BackgroundThe role of circular RNAs (circRNAs) in the occurrence of gastric cancer is still unclear. Therefore, the diagnostic value and mechanisms underlying hsa_circ_0061276 in the occurrence of gastric cancer were explored.MethodsReverse transcription-droplet digital polymerase chain reaction was used to detect the copy number of hsa_circ_0061276 in plasma from healthy individuals, as well as from patients with gastric precancerous lesions or early-stage or advanced gastric cancer. Plasmids overexpressing or knocking down hsa_circ_0061276 expression were transfected into gastric cancer cells. The effects on the growth, migration, and cell cycle distribution of gastric cancer cells were then analyzed. Finally, miRanda and RNAhybrid were used to explore the binding sites between hsa_circ_0061276 and microRNAs (miRNAs). A double luciferase reporter gene assay was used to confirm the miRNA sponge effect.ResultsThe results show that plasma hsa_circ_0061276 copy number showed a trend of a gradual decrease when comparing healthy controls to the early cancer group and advanced gastric cancer group. Overexpression of hsa_circ_0061276 inhibited the growth and migration of gastric cancer cells. Through bioinformatic analyses combined with cellular experiments, it was found that hsa_circ_0061276 inhibited the growth of gastric cancer by binding to hsa-miR-7705.ConclusionHsa_circ_0061276 may be a new biomarker for gastric cancer. The tumor suppressor role of hsa_circ_0061276 on gastric cancer likely occurs through a sponge effect on miRNAs such as hsa-miR-7705.

Yue Li, Chen Chen, Hai‐Lin Liu et al.

Abstract Background Pazopanib is an approved multitarget anticancer agent for soft tissue sarcoma (STS) and renal cell carcinoma (RCC), which is also under clinical investigation for other malignancies, including small cell lung cancer (SCLC). However, the potential anti‐SCLC mechanisms of pazopanib remain unclear. Methods Cell viability was evaluated by CCK‐8, apoptotic cell detection was conducted using annexin V/PI staining followed by flow cytometry, and Western blot analysis was used to detect the apoptotic‐related molecules and ER‐stress pathway effectors. The intracellular reactive oxygen species (ROS) level was determined by DCFH‐HA staining followed by flow cytometry. An NCI‐H446 xenograft model was established to evaluate pazopanib on tumor suppression in vivo. Immunohistochemistry (IHC) was used to assess the proliferative activity of xenograft in NCI‐H446 cell‐bearing NOD‐SCID mice. Results Pazopanib dose‐ and time‐dependently inhibited SCLC cell proliferation induced significant apoptosis in SCLC cell lines, increased cleaved‐caspase3 and Bax, and decreased Bcl‐2. Moreover, the PERK‐related ER‐stress pathway was potently activated by pazopanib treatment, inhibiting ER‐stress by salubrinal significantly reversing pazopanib‐mediated apoptosis in SCLC cell lines. Furthermore, pazopanib‐induced intracellular ROS levels increased, while inhibiting ROS by NAC significantly reversed pazopanib‐induced apoptosis in SCLC cells. In addition, pazopanib significantly suppressed NCI‐H446 xenograft growth and decreased Ki67 positive cells in the tumor. Conclusion Our findings indicate that pazopanib induces SCLC cell apoptosis through the ER‐stress process via upregulation of ROS levels. Further investigation of relevant biomarkers to accurately select patients for benefit from pazopanib should be further investigated.

Zachary Compton, Kathleen Hanlon, Carolyn C. Compton et al.

Cancer cells possess a nearly universal set of characteristics termed the hallmarks of cancer, including replicative immortality and resisting cell death. Dysregulated differentiation is present in virtually all cancers yet has not yet been described as a cancer hallmark. Like other hallmarks, dysregulated differentiation involves a breakdown of the cellular cooperation that typically makes multicellularity possible - in this case disrupting the division of labor among the cells of a body. At the time that the original hallmarks of cancer were described, it was not known that dysregulated differentiation was mechanistically distinct from growth inhibition, but now that this is known, it is a further reason to consider dysregulated differentiation a hallmark of cancer. Dysregulated differentiation also has clinical utility, as it forms the basis of pathological grading, predicts clinical outcomes, and is a viable target for therapies aimed at inducing differentiation. Here we argue that hallmarks of cancer should be near universal, mechanistically distinct, and have clinical utility for prognosis and/or therapy. Dysregulated differentiation meets all of these criteria.

L. Budde, L. Sehn, M. Matasar et al.

Background: FL is associated with frequent relapses and decreasing progression-free intervals with successive lines of conventional therapy. Later-line treatments may be less effective due to refractory disease. Mosunetuzumab (Mosun) is a CD20xCD3 bispecific antibody (Ab) that redirects T cells to eliminate malignant B cells. In the dose-escalation phase of an ongoing Phase I/II study (NCT02500407), Mosun was highly active and well tolerated in R/R FL patients (pts) who had received ≥2 prior lines of therapy (3L+ R/R FL) when given IV with Cycle (C) 1 step-up dosing for mitigation of cytokine release syndrome (CRS; Assouline et al. ASH 2020). We present pivotal data from the same study from a large expansion cohort of 3L+ R/R FL pts who received Mosun monotherapy at the recommended Phase II dose (1/2/60/30mg). Methods: 3L+ R/R FL pts were enrolled into a single-arm, pivotal expansion cohort. All pts had FL (Grade [Gr] 1-3a), ECOG PS ≤1, and were R/R to ≥2 prior lines of therapy including an anti (a)-CD20 Ab and an alkylator. Mosun was given IV in 21-day cycles with step-up dosing in C1 (C1 Day [D]1: 1mg; C1D8: 2mg; C1D15 and C2D1: 60mg; D1 C3+: 30mg). Pts who achieved a complete response (CR) by C8 discontinued therapy; those with a partial response or stable disease continued treatment for a total of 17 cycles, unless disease progression (PD) or unacceptable toxicity occurred. The primary endpoint was CR (as best response) rate by PET/CT assessed by an independent review facility (IRF) using standard response criteria (Cheson et al. J Clin Oncol 2007). No mandatory hospitalization was required. Results: A total of 90 pts were enrolled (median age: 60 years, range: 29-90; 61.1% male). At entry, 76.7% had stage III or IV disease and 44.4% had FLIPI 3-5. Median number of prior lines of therapy was 3 (range: 2-10). In addition to aCD20 Abs and alkylators (all pts), prior cancer therapies included anthracyclines (82.2%), ASCT (21.1%), PI3K inhibitors (18.9%), IMiDs (14.4%), BTK inhibitors (6.7%), and CAR-Ts (3.3%). 68.9% of pts were refractory to their last therapy, 78.9% to any prior aCD20 Ab, and 53.3% to any prior aCD20 Ab and an alkylator (double refractory). 52.2% had PD within 24 months from the start of initial therapy (POD24). As of March 15, 2021, median time on study was 12.9 months (range: 2.0-22.1). Anti-tumor activity was seen in most pts (Figure). Best objective response (ORR) and CR rates by IRF were 78.9% (71/90 pts) and 57.8% (52/90), respectively (Table); the median time to first response was 1.4 months. Best ORR and CR rates were generally consistent in pre-specified subgroups (Table), including POD24 (ORR: 83%; CR: 55%) and double-refractory pts (ORR: 69%; CR: 48%). Median duration of objective response and CR was not reached; 12-month event-free rates after first response were 65.4% (95% CI: 52.6-78.1%) in all responders and 80.1% (95% CI: 67.4-92.7%) in CR pts. Median PFS was 17.9 months (95% CI: 12.0-not estimable). CRS (Lee et al. Biol Blood Marrow Transplant 2019) was the most common adverse event (AE; 44.4% of pts). CRS was mostly confined to C1 and generally low Gr (Gr 1: 25.6%; Gr 2: 16.7%). High Gr CRS was uncommon (Gr 3: 1 patient; Gr 4: 1 patient with FL in leukemic phase); no Gr 5 CRS occurred. In the 40 pts with CRS, tocilizumab was used in 7 pts and corticosteroids in 9 pts; all events resolved after a median duration of 3 days. Other common (≥20%) AEs were fatigue (36.7%), headache (31.1%), neutropenia and pyrexia (28.9% each), hypophosphatemia (22.2%), and pruritus (21.1%). Common (≥5%) Gr 3-4 AEs (66.7% overall) were neutropenia (26.6%), hypophosphatemia (13.3%), hyperglycemia and anemia (7.8% each), and elevated ALT (5.6%). Gr 3 neurologic events were uncommon (4.4%) and no Gr 4-5 events occurred. Common (≥5%) SAEs (45.6% overall) were CRS (23.3%, including 2.2% Gr 3-4 CRS). Two Gr 5 (fatal) AEs occurred (malignant neoplasm progression and unexplained death; both considered unrelated to Mosun by investigators). AEs leading to Mosun discontinuation were uncommon (4 pts; 4.4%). Conclusions: Mosun induces deep and durable remissions in 3L+ R/R FL pts, including those with POD24 and/or double-refractory disease. High response rates are achieved (ORR: 78.9%; CR: 57.8%) and maintained for ≥12 months in the majority of pts. Mosun has a manageable safety profile, with C1 step-up dosing effectively mitigating CRS, enabling treatment without mandatory hospitalization. Mosun represents an active new therapy for 3L+ R/R FL. Figure 1 Figure 1. Budde: Merck, Inc: Research Funding; Amgen: Research Funding; AstraZeneca: Research Funding; Mustang Bio: Research Funding; Novartis: Consultancy; Gilead: Consultancy; F. Hoffmann-La Roche Ltd: Consultancy; BeiGene: Consultancy; Genentech, Inc.: Consultancy. Sehn: Novartis: Consultancy; Genmab: Consultancy; Debiopharm: Consultancy. Matasar: Teva: Consultancy; Rocket Medical: Consultancy, Research Funding; Bayer: Consultancy, Honoraria, Research Funding; Merck: Consultancy; Juno Therapeutics: Consultancy; Takeda: Consultancy, Honoraria; Pharmacyclics: Honoraria, Research Funding; Genentech, Inc.: Consultancy, Honoraria, Research Funding; F. Hoffmann-La Roche Ltd: Consultancy, Honoraria, Research Funding; TG Therapeutics: Consultancy, Honoraria; Merck Sharp & Dohme: Current holder of individual stocks in a privately-held company; Memorial Sloan Kettering Cancer Center: Current Employment; Daiichi Sankyo: Consultancy; Janssen: Honoraria, Research Funding; IGM Biosciences: Research Funding; Seattle Genetics: Consultancy, Honoraria, Research Funding; ImmunoVaccine Technologies: Consultancy, Honoraria, Research Funding; GlaxoSmithKline: Honoraria, Research Funding. Schuster: Novartis: Consultancy, Honoraria, Patents & Royalties, Research Funding; Abbvie: Consultancy, Research Funding; Pharmacyclics: Research Funding; Merck: Research Funding; Genentech/Roche: Consultancy, Research Funding; Tessa Theraputics: Consultancy; Loxo Oncology: Consultancy; Juno Theraputics: Consultancy, Research Funding; BeiGene: Consultancy; Alimera Sciences: Consultancy; Acerta Pharma/AstraZeneca: Consultancy; Adaptive Biotechnologies: Research Funding; Incyte: Research Funding; TG Theraputics: Research Funding; Nordic Nanovector: Consultancy; Celgene: Consultancy, Honoraria, Research Funding. Assouline: Pfizer: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; AbbVie: Consultancy, Honoraria, Research Funding, Speakers Bureau; AstraZeneca: Consultancy, Honoraria; F. Hoffmann-La Roche Ltd: Consultancy, Honoraria, Research Funding; BeiGene: Consultancy, Honoraria, Research Funding; Takeda: Research Funding; Roche/Genentech: Research Funding; Jewish General Hospital, Montreal, Quebec: Current Employment; Eli Lilly: Research Funding; Novartis: Honoraria, Research Funding; Amgen: Current equity holder in publicly-traded company, Research Funding; Gilead: Speakers Bureau; Johnson&Johnson: Current equity holder in publicly-traded company. Giri: Royal Adelaide Hospital: Current Employment. Kuruvilla: AbbVie: Honoraria; Amgen: Honoraria; Antengene: Honoraria; Janssen: Honoraria, Research Funding; Merck: Honoraria; AstraZeneca: Honoraria, Research Funding; Medison Ventures: Honoraria; TG Therapeutics: Honoraria; Seattle Genetics: Honoraria; Incyte: Honoraria; Karyopharm: Honoraria, Other: Data and Safety Monitoring Board; Roche: Honoraria, Research Funding; Gilead: Honoraria; Pfizer: Honoraria; Novartis: Honoraria; BMS: Honoraria. Canales: iQone: Honoraria; Novartis: Consultancy, Honoraria; Sanofi: Consultancy; Celgene/Bristol-Myers Squibb: Consultancy, Honoraria; Karyopharm: Consultancy, Honoraria; Incyte: Consultancy; Takeda: Consultancy, Honoraria, Speakers Bureau; F. Hoffmann-La Roche Ltd: Consultancy, Honoraria, Speakers Bureau; Sandoz: Honoraria, Speakers Bureau; Eusa Pharma: Consultancy, Honoraria; Janssen: Consultancy, Honoraria, Speakers Bureau; Gilead/Kite: Consultancy, Honoraria. Dietrich: University Hospital Heidelberg: Current Employment; F. Hoffmann-La Roche Ltd: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Janssen: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; KITE/Gilead: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Incyte: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Fay: St Vincent's Hosptial, Sydney, Australia: Current Employment. Ku: Roche: Consultancy; Antegene: Consultancy; Genor Biopharma: Consultancy. Nastoupil: Epizyme: Honoraria, Research Funding; MorphoSys: Honoraria; Janssen: Honoraria, Research Funding; ADC Therapeutics: Honoraria; Bristol Myers Squibb/Celgene: Honoraria, Research Funding; Genentech: Honoraria, Research Funding; IGM Biosciences: Research Funding; Caribou Biosciences: Research Funding; Gilead/Kite: Honoraria, Research Funding; Denovo Pharma: Other: DSMC; Takeda: Honoraria, Other: DSMC, Research Funding; Novartis: Honoraria, Research Funding; Bayer: Honoraria; TG Therapeutics: Honoraria, Research Funding; Pfizer: Honoraria, Research Funding. Wei: Genentech, Inc.: Current Employment; F. Hoffmann-La Roche Ltd: Current equity holder in publicly-traded company, Current holder of individual stocks in a privately-held company, Current holder of stock options in a privately-held company. Yin: Genentech, Inc.: Current Employment, Current equity holder in publicly-traded company, Divested equity in a private or publicly-traded company in the past 24 months. Doral: Genentech, Inc.: Current Employment; F. Hoffmann-La Roche Ltd: Current equity holder in publicly-traded company, Divested equity in a private or publicly-traded company in the past 24 months. Li: Genentech, Inc.: Current Employment, Current holder of individual stocks in a privately-held company. Huang: F. Hoffmann-La Roche Ltd: Current Employment. Negricea: F. Hoffmann-La Roche Ltd: Current Employment, Current e

Andrew Dunbar, Young Park, Ross Levine

INTRODUCTION Patients (pts) with blood disorders are at particular risk for severe infection and death from COVID-19. Factors that contribute to this risk, including cancer treatment, have not been clearly delineated. The ASH RC COVID-19 Registry for Hematology is a public-facing, volunteer registry reporting outcomes of COVID-19 infection in pts with underlying blood disorders. We report a multivariable analysis of the impact of cancer treatment and other key variables on COVID-19 mortality and hospitalization among pts with blood cancer. METHODS Data were collected between April 1, 2020, and July 2, 2021. All analyses were performed using R version 4.0.2. Multivariable logistic regression explored associations between mortality and seven patient/disease factors previously reported as important to COVID-19 outcome. Independent variables included: age (>60); sex; presence of a major comorbidity (defined as any of heart disease, hypertension, pulmonary disease and/or diabetes); type of hematologic malignancy; estimated prognosis of 90y). The sample was 42% female and 28% had major comorbidities. Types of hematologic malignancies were 354 (34%) acute leukemia/MDS, 255 (25%) lymphoma, 206 (20%) plasma cell dyscrasia (myeloma/amyloid/POEMS), 116 (11%) CLL, 98 (10%) myeloproliferative neoplasm (MPN). Most pts (73%) received cancer treatment during the previous year, 9% had a pre-COVID-19 prognosis of 60 (OR 2.03, 1.31-3.18), male sex (OR 1.69, 1.11 - 2.61), estimated pre-COVID-19 prognosis of less than 6 months (OR 6.16, 3.26 - 11.70) and ICU deferral (OR 10.87, 6.36 - 18.96) were all independently associated with an increased risk of death. Receiving cancer treatment in the year prior to COVID-19 diagnosis and type of hematologic malignancy were not significantly associated with death. In multivariable analyses, age > 60 (OR 2.46, 1.83 - 3.31), male sex (OR 1.34, 1.02 - 1.76), estimated pre-COVID-19 prognosis of 60, male sex, pre-COVID-19 prognosis of < 6 months, and deferral of ICU care on mortality among patients with hematologic malignancy and COVID-19. We did not observe an increased risk of COVID-19 mortality among pts with COVID-19 who received blood cancer treatment in the previous year, although rate of hospitalization was higher. Pts with some hematologic malignancies (MPN, plasma cell dyscrasias), may experience less severe COVID-19 infections than others. Disclosures Anderson:  Celgene: Membership on an entity's Board of Directors or advisory committees; Millenium-Takeda: Membership on an entity's Board of Directors or advisory committees; Gilead: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees; Sanofi-Aventis: Membership on an entity's Board of Directors or advisory committees; Bristol Myers Squibb: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees; Scientific Founder of Oncopep and C4 Therapeutics: Current equity holder in publicly-traded company, Current holder of individual stocks in a privately-held company; AstraZeneca: Membership on an entity's Board of Directors or advisory committees; Mana Therapeutics: Membership on an entity's Board of Directors or advisory committees. Desai:  Janssen R&D: Research Funding; Astex: Research Funding; Kura Oncology: Consultancy; Agios: Consultancy; Bristol Myers Squibb: Consultancy; Takeda: Consultancy. Goldberg:  Celularity: Research Funding; Genentech: Consultancy, Membership on an entity's Board of Directors or advisory committees; Astellas: Consultancy, Membership on an entity's Board of Directors or advisory committees; Aptose: Consultancy, Research Funding; Prelude Therapeutics: Research Funding; DAVA Oncology: Honoraria; Pfizer: Research Funding; Arog: Research Funding; Aprea: Research Funding; AbbVie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding. Neuberg:  Madrigal Pharmaceuticals: Other: Stock ownership; Pharmacyclics: Research Funding. Radhakrishnan:  Janssen India: Honoraria; Dr Reddy's Laboratories: Honoraria, Membership on an entity's Board of Directors or advisory committees; Aurigene: Speakers Bureau; Novartis: Honoraria; Johnson and Johnson: Honoraria; Pfizer: Consultancy, Honoraria; Astrazeneca: Consultancy, Honoraria; Emcure Pharmaceuticals: Other: payment to institute; Cipla Pharmaceuticals: Honoraria, Other: payment to institute; Bristol Myers Squibb: Other: payment to institute; Roche: Honoraria, Other: payment to institute; Intas Pharmaceutical: Other: payment to institute; NATCO Pharmaceuticals: Research Funding. Sehn:  Genmab: Consultancy; Debiopharm: Consultancy; Novartis: Consultancy. Sekeres:  Novartis: Membership on an entity's Board of Directors or advisory committees; Takeda/Millenium: Membership on an entity's Board of Directors or advisory committees; BMS: Membership on an entity's Board of Directors or advisory committees. Tallman:  Kura: Membership on an entity's Board of Directors or advisory committees; Syros: Membership on an entity's Board of Directors or advisory committees; Innate Pharma: Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees; Biosight: Membership on an entity's Board of Directors or advisory committees; Roche: Membership on an entity's Board of Directors or advisory committees; Jazz Pharma: Membership on an entity's Board of Directors or advisory committees; Oncolyze: Membership on an entity's Board of Directors or advisory committees; KAHR: Membership on an entity's Board of Directors or advisory committees; Orsenix: Membership on an entity's Board of Directors or advisory committees; Daiichi-Sankyo: Membership on an entity's Board of Directors or advisory committees; Abbvie: Membership on an entity's Board of Directors or advisory committees; Amgen: Research Funding; Rafael Pharmaceuticals: Research Funding; Glycomimetics: Research Funding; Biosight: Research Funding; Orsenix: Research Funding; Abbvie: Research Funding; NYU Grand Rounds: Honoraria; Mayo Clinic: Honoraria; UC DAVIS: Honoraria; Northwell Grand Rounds: Honoraria; NYU Grand Rounds: Honoraria; Danbury Hospital Tumor Board: Honoraria; Acute Leukemia Forum: Honoraria; Miami Leukemia Symposium: Honoraria; New Orleans Cancer Symposium: Honoraria; ASH: Honoraria; NCCN: Honoraria.

Maolong Wang, Minge Ma, Yuling Yang et al.

Abstract Background Circular RNAs (circRNAs) had been identified as a non‐coding RNA associated with many types of cancer in recent years. However, the involvement of hsa_circ_0008274 in lung adenocarcinoma (LUAD) has not been explored. The aim of our research is to explore the biological mechanism and function of hsa_circ_0008274 in LUAD. Methods The hsa_circ_0008274, miR‐578, and high mobility group AT‐Hook 2 (HMGA2) mRNA expression levels were detected via qRT‐PCR. Cell Counting Kit‐8 (CCK‐8) Transwell assay and wound healing assay were performed to measure the cell proliferation, invasion, and migration ability. Luciferase reporter and Western blotting experiments were performed to characterize the competing endogenous RNA (ceRNA) mechanism of hsa_circ_0008274. Results Our findings determined that the expression of hsa_circ_0008274 in LUAD was significantly decreased. Cell experiments showed that overexpressed hsa_circ_0008274 could reduce the proliferation and invasion ability of LUAD cells. Moreover, miRNA‐578 could identify as a miRNA sponge of hsa_circ_0008274. Overexpressed hsa_circ_0008274 reduced the proliferation and invasion of LUAD cells caused by miR‐578 mimics. Increasing the expression of miR‐578 can aggravate the proliferation and invasion of LUAD cells and block the inhibition of proliferation and invasion of LUAD cells mediated by overexpressed hsa_circ_0008274. Subsequent data indicate that HMGA2 of the tumor‐promoting gene is the target gene of miR‐578. The upregulation of HMGA2 partially reversed the tumor inhibitory effect of LUAD cells induced by overexpressed hsa_circ_0008274 or miR‐578 mimics. Conclusions In summary, our data show that the overexpression of hsa_circ_0008274 repressed the proliferation and invasion of LUAD through downregulating miR‐578 and activating HMGA2.

Inge van denBerg, Marcel Smid, Robert R. J. Coebergh van den Braak et al.

Consensus molecular subtypes (CMSs) can guide precision treatment of colorectal cancer (CRC). We aim to identify methylation markers to distinguish between CMS2 and CMS3 in patients with CRC, for which an easy test is currently lacking. To this aim, fresh‐frozen tumor tissue of 239 patients with stage I‐III CRC was analyzed. Methylation profiles were obtained using the Infinium HumanMethylation450 BeadChip. We performed adaptive group‐regularized logistic ridge regression with post hoc group‐weighted elastic net marker selection to build prediction models for classification of CMS2 and CMS3. The Cancer Genome Atlas (TCGA) data were used for validation. Group regularization of the probes was done based on their location either relative to a CpG island or relative to a gene present in the CMS classifier, resulting in two different prediction models and subsequently different marker panels. For both panels, even when using only five markers, accuracies were > 90% in our cohort and in the TCGA validation set. Our methylation marker panel accurately distinguishes between CMS2 and CMS3. This enables development of a targeted assay to provide a robust and clinically relevant classification tool for CRC patients.

Peh Joo Ho, Fuh Yong Wong, Wen Yee Chay et al.

Abstract Background Breast cancer incidence is increasing in Asia. However, few women in Singapore attend routine mammography screening. We aim to identify women at high risk of breast cancer who will benefit most from regular screening using the Gail model and information from their first screen (recall status and mammographic density). Methods In 24,431 Asian women (50–69 years) who attended screening between 1994 and 1997, 117 developed breast cancer within 5 years of screening. Cox proportional hazard models were used to study the associations between risk classifiers (Gail model 5‐year absolute risk, recall status, mammographic density), and breast cancer occurrence. The efficacy of risk stratification was evaluated by considering sensitivity, specificity, and the proportion of cancers identified. Results Adjusting for information from first screen attenuated the hazard ratios (HR) associated with 5‐year absolute risk (continuous, unadjusted HR [95% confidence interval]: 2.3 [1.8–3.1], adjusted HR: 1.9 [1.4–2.6]), but improved the discriminatory ability of the model (unadjusted AUC: 0.615 [0.559–0.670], adjusted AUC: 0.703 [0.653–0.753]). The sensitivity and specificity of the adjusted model were 0.709 and 0.622, respectively. Thirty‐eight percent of all breast cancers were detected in 12% of the study population considered high risk (top five percentile of the Gail model 5‐year absolute risk [absolute risk ≥1.43%], were recalled, and/or mammographic density ≥50%). Conclusion The Gail model is able to stratify women based on their individual breast cancer risk in this population. Including information from the first screen can improve prediction in the 5 years after screening. Risk stratification has the potential to pick up more cancers.