Citations

AbstractBackgroundComputed tomography (CT) is routinely used to guide cryoablation procedures. Notably, CT‐guidance provides 3D localization of cryoprobes and can be used to delineate frozen tissue during ablation. However, metal‐induced artifacts from ablation probes can make accurate probe placement challenging and degrade the ice ball conspicuity, which in combination could lead to undertreatment of potentially curable lesions.PurposeIn this work, we propose an image‐based neural network (CNN) model for metal artifact reduction for CT‐guided interventional procedures.MethodsAn image domain metal artifact simulation framework was developed and validated for deep‐learning‐based metal artifact reduction for interventional oncology (MARIO). CT scans were acquired for 19 different cryoablation probe configurations. The probe configurations varied in the number of probes and the relative orientations. A combination of intensity thresholding and masking based on maximum intensity projections (MIPs) was used to segment both the probes only and probes + artifact in each phantom image. Each of the probe and probe + artifact images were then inserted into 19 unique patient exams, in the image domain, to simulate metal artifact appearance for CT‐guided interventional oncology procedures. The resulting 361 pairs of simulated image volumes were partitioned into disjoint training and test datasets of 304 and 57 volumes, respectively. From the training partition, 116 600 image patches with a shape of 128 × 128 × 5 pixels were randomly extracted to be used for training data. The input images consisted of a superposition of the patient and probe + artifact images. The target images consisted of a superposition of the patient and probe only images. This dataset was used to optimize a U‐Net type model. The trained model was then applied to 50 independent, previously unseen CT images obtained during renal cryoablations. Three board‐certified radiologists with experience in CT‐guided ablations performed a blinded review of the MARIO images. A total of 100 images (50 original, 50 MARIO processed) were assessed across different aspects of image quality on a 4‐point likert‐type item. Statistical analyses were performed using Wilcoxon signed‐rank test for paired samples.ResultsReader scores were significantly higher for MARIO processed images compared to the original images across all metrics (all p < 0.001). The average scores of the overall image quality, iceball conspicuity, overall metal artifact, needle tip visualization, target region confidence, and worst metal artifact, needle tip visualization, iceball conspicuity, and target region confidence improved by 34.91%, 36.29%, 39.94%, 34.17%, 35.13%, and 45.70%, respectively.ConclusionsThe proposed method of image‐based metal artifact simulation can be used to train a MARIO algorithm to effectively reduce probe‐related metal artifacts in CT‐guided cryoablation procedures.

Introduction: ECOG ACRIN E1910 is a randomized phase III trial that showed that adults with newly diagnosed BCR::ABL1 negative acute lymphoblastic leukemia (ALL) who become MRD negative (&lt;0.01%) after induction chemo who receive blinatumomab with conventional chemotherapy (chemo) have improved survival compared with those who received chemo only (Litzow et al, Blood (2022) 140: Supplement 2, LBA-1). However, not all pts were able to receive all four planned cycles of blinatumomab in consolidation. In this report we assessed outcomes of pts in the blinatumomab arm of the trial who received all 4 cycles of blinatumomab compared to those who received 1-2 cycles. Methods: Patient between the ages of 30 and 70 with newly diagnosed BCR::ABL1 negative B-lineage ALL were enrolled and initially received 2.5 months of combination induction chemo utilizing a BFM-like regimen adapted from the E2993/UKALLXII clinical trial, with pegaspargase (after induction only for patients &gt;=55 years of age) and addition of rituximab for CD20 positive patients. After remission induction (step 1) pts in morphologic complete remission (CR/CRi) received an intensification course of high dose methotrexate with pegaspargase for CNS prophylaxis (step 2). At the conclusion of step 2, remission and MRD status were determined centrally by 6-color flow cytometry with MRD negativity defined as &lt;0.01%. In the primary analysis subset, MRD negative patients were randomized to receive an additional 4cycles of consolidation chemo or 2 cycles of blinatumomab at 28 mcg/day (flat dose) for 28 days each cycle followed by 3 cycles of consolidation chemo, another 4-week cycle of blinatumomab (3rd cycle of blinatumomab) followed by an additional cycle of chemo and then a 4th cycle of blinatumomab (step 3). Following completion of consolidation chemo +/- blinatumomab, pts were given 2.5 years of POMP maintenance therapy timed from the start of the intensification cycle (step 4). Estimates of OS were calculated using the Kaplan-Meier method. Comparison of OS between treatment arms were conducted using the stratified log-rank test and Cox proportional-hazards model with age, CD20 status, rituximab use, and whether pts intend to receive HSCT or not as stratification factors. As the number of cycles needed to optimize outcome has not been studied, an unplanned landmark analysis was used to compare OS of pts in the blinatumomab arm who received all 4 cycles o those who received 1-2 cycles. Time 0 was chosen as 9 months post step 3 randomization (the time that patients were supposed to complete 4 cycles of blina). Four pts who received blinatumomab but died within 9 months post step 3 randomization were therefore excluded from this analysis. Results: The study was activated on December 17, 2013 and closed to enrollment on October 15, 2019. 772 pts were screened for the trial and 488 were enrolled on step 1 induction therapy. The median age of the pts was 51 years (range 30-70). 224 MRD negative pts were randomized, 112 pts to each arm. In the blinatumomab arm 12 pts received 1 cycle (11%), 32 pts received 2 cycles (29%), 4 pts received 3 cycles (4%) and 63 pts received 4 cycles (57%). The OS of pts who received 1-2 cycles of blinatumomab was compared to control (Figure 1) The difference was not significant (hazard ratio 0.62, 95% CI 0.28 to 1.34, p=0.22). Fig 2 compares the survival of those who received 1-2 cycles to those who received 4 cycles ( (HR: 0.39, 95% CI 0.12 to 1.16, p=0.076). Conclusion: The addition of blinatumomab to consolidation chemotherapy resulted in a significantly better overall survival compared to chemotherapy alone in pts with newly diagnosed B-lineage ALL who were MRD negative after intensification. This represents a new standard of care for pts aged 30-70 years with BCR::ABL1 negative ALL. The optimal dose and number of cycles is however unknown. In this unplanned subgroup analysis we demonstrate that a survival benefit can only be confirmed in patients who receive the intended 4 cycles of blinatumomab during consolidation. Further studies will be needed to determine if fewer cycles can provide a similar benefit.

Gene fusions involving tumor protein p63 gene (TP63) occur in multiple T and B cell lymphomas and portend a dismal prognosis for patients. The function and mechanisms of TP63 fusions remain unclear, and there is no target therapy for patients with lymphoma harboring TP63 fusions. Here, we show that TP63 fusions act as bona fide oncogenes and are essential for fusion-positive lymphomas. Transgenic mice expressing TBL1XR1::TP63, the most common TP63 fusion, develop diverse lymphomas that recapitulate multiple human T and B cell lymphomas. Here, we identify that TP63 fusions coordinate the recruitment of two epigenetic modifying complexes, the nuclear receptor corepressor (NCoR)—histone deacetylase 3 (HDAC3) by the N-terminal TP63 fusion partner and the lysine methyltransferase 2D (KMT2D) by the C-terminal TP63 component, which are both required for fusion-dependent survival. TBL1XR1::TP63 localization at enhancers drives a unique cell state that involves up-regulation of MYC and the polycomb repressor complex 2 (PRC2) components EED and EZH2. Inhibiting EZH2 with the therapeutic agent valemetostat is highly effective at treating transgenic lymphoma murine models, xenografts, and patient-derived xenografts harboring TP63 fusions. One patient with TP63 -rearranged lymphoma showed a rapid response to valemetostat treatment. In summary, TP63 fusions link partner components that, together, coordinate multiple epigenetic complexes, resulting in therapeutic vulnerability to EZH2 inhibition.

Abstract Recurrent chromosomal rearrangements are a hallmark of hematologic malignancies and play critical roles in pathogenesis. The TP53 analog TP63 is rearranged in 5-10% of diverse subtypes of both aggressive T- and B-cell lymphomas. Patients with TP63-rearranged lymphomas have dismal outcomes, with 5-year overall survival rates between 0-17%, depending on cohorts. The function and mechanisms of TP63 rearrangements and TP63 fusion proteins in tumorigenesis are poorly understood. As a result, attempts to treat these patients to date have been largely empiric. Thus, there is an urgent need to understand how TP63 fusions contribute to tumorigenesis and to translate the findings into novel therapeutic options for these patients. Here, we demonstrated that TP63 fusions are essential for the propagation of T-cell lymphomas (TCLs). Knockdown of TP63 fusions with specific shRNAs in TCL cell lines harboring TP63 fusions suppressed both cell growth in vitro and tumor growth in vivo. Retroviral expression of TBL1XR1-TP63, the most common TP63 fusion, conferred cytokine independence in Ba/F3 cells, consistent with its role as an oncogene. To investigate the role of TP63 fusions in T- and B-cell lymphomagenesis, we engineered a CAG-Loxp-Stop-Loxp-TBL1XR1-TP63 conditional knock-in mouse model and crossed with hCD2-Cre mice. This results in expression beginning during early lymphoid development. As observed in patients, transgenic mice developed multiple subtypes of both T- and B-cell lymphoma. To define the effects and mechanisms of TP63 fusions within T cells, we performed CRISPR scanning, transcriptomic, epigenomic, and proteomic analyses. Our data showed that domains within both the N-terminal TBL1XR1 and C-terminal TP63 portions contribute to the function of this fusion. We found that the N-terminal component of TP63 fusions interacts with components of the NCOR/SMRT complex. At the same time, the C-terminal portion of TP63 (which recapitulates the deltaN-p63 isoform expressed in some carcinomas) interacts with the enhancer modifier KMT2D and its complex members. TBL1XR1-TP63 binds to a novel distal enhancer to drive MYC expression, and thus upregulates the expression of the histone H3K27 methylase EZH2. Finally, we assessed whether EZH2 is a vulnerability of TP63-rearranged lymphomas. We found that knockdown of EZH2 in TP63-rearranged lines significantly impaired cell growth, as did treatment with the EZH2 and 1 dual inhibitor valemetostat. Valemetostat, which is now being tested in patients with lymphoma, counteracted the oncogenic effects of TP63 fusions in multiple preclinical models in vivo. Together, our results identify the TP63 fusion as a highly unique oncogenic driver in lymphomagenesis capable of recruiting multiple epigenetic modifier complexes and inducing a targetable dependence on EZH2. Citation Format: Gongwei Wu, Noriaki Yoshida, Jihe Liu, Xiaoyang Zhang, Yuan Xiong, Tayla Heavican-Foral, Huiyun Liu, Geoffrey Nelson, Lu Yang, Renee Chen, Katherine Donovan, Marcus Jones, Mikhail Roshal, Yanming Zhang, Ran Xu, Ajit Nirmal, Salvia Jain, Catharine Leahy, Kristen Jones, Kristen Stevenson, Natasha Galasso, Nivetha Ganesan, Tiffany Chang, Wen-Chao Wu, Abner Louissaint, Lydie Debaize, Hojong Yoon, Paola Dal Cin, Wing Chan Chan, Shannan Ho Sui Ho Sui, Samuel Ng, Andrew Feldman, Steven M. Horwitz, Mathew Meyerson, Karen Adelman, Eric Fischer, Chun-Wei Chen, David Weinstock, Myles Brown. TP63 fusions drive enhancer rewiring, lymphomagenesis, and dependence on EZH2. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5755.