Citations

Abstract Tissue-mimicking phantoms play a crucial role in evaluating new imaging modalities, simulating clinical scenarios, and ensuring the quality and reliability of imaging systems. Styrene–ethylene/butylene–styrene (SEBS) is an extremely stable and versatile material that has been recently used in ultrasound, elastography, and photoacoustic imaging across diverse applications. Despite its extensive use, a systematic viscoelastic characterization of different SEBS-based formulations reported in the literature, particularly using shear wave elastography (SWE), remains limited. To address this gap, this study investigates the viscoelastic properties of tissue-mimicking phantoms composed of SEBS, glycerol dispersions in SEBS gels, and mixtures of SEBS with low-density polyethylene (LDPE) in mineral oil, using gelatin phantoms as a reference for comparison. Viscoelastic parameters were estimated from shear wave velocity and attenuation dispersion curves using a Kelvin–Voigt model, based on SWE measurements with acoustic radiation force (ARF) excitation, shear plane-wave excitation, and complementary mechanical testing. SEBS-based phantoms exhibited broad, composition-dependent viscoelastic tunability. Increasing SEBS or LDPE content led to stiffer and more viscous responses, while glycerol primarily modulated viscosity and, at higher concentrations, also affected stiffness. Consistent and concordant values were obtained using both ARF and plane wave SWE techniques. The resulting stiffness (2–22 kPa) and viscosity (0.01–1.24 Pa⋅s) overlaps with the mechanical properties of several biological soft tissues, supporting use of these phantoms for SWE validation.

Abstract Background Cardiomegaly is a clinically significant incidental finding on chest computed tomography (CT) associated with heart failure, arrhythmias, and sudden cardiac death. Qualitative radiologist assessment is variable, and automated AI tools may enable objective opportunistic cardiac volumetry. Purpose To evaluate whether AI-enabled total cardiac volume (TCVAI) derived from non-ECG-gated, non-contrast chest CT can identify cardiomegaly as defined by echocardiography. Materials and Methods This retrospective study included 307 consecutive patients (median age, 67 years; 56% male) who underwent non-contrast chest CT at a single center on 7 scanner types (4 vendors) and clinically indicated echocardiography within 31 days. A commercially available AI tool (AI-Rad Companion, Siemens Healthineers) automatically quantified TCVAI, indexed to body surface area (TCVAI/BSA). Echocardiography reports were reviewed for chamber dilation and left ventricular hypertrophy (LVH), collectively defined as cardiomegaly. Associations between TCVAI/BSA and echocardiographic findings were assessed using correlation, ordinal regression, and receiver operating characteristic (ROC). Interscan repeatability was evaluated in 248 patients with 544 repeat CT examinations. Prespecified sex-specific thresholds were tested in a temporally independent validation cohort of 50 patients. Results Median TCVAI was higher in patients with cardiomegaly than those without (1061.9 vs 798.4 mL; P < .001). TCVAI/BSA was associated with chamber dilation and LVH severity on univariate analysis and remained associated in multivariable ordinal models, except for right ventricular dilation. Discriminatory performance was fair to good, with area under the curve (AUC) 0.81 (95% CI, 0.75-0.87) in men and 0.77 (95% CI, 0.69-0.85) in women. Interscan repeatability was excellent (intraclass correlation coefficient [ICC]: 0.93). In independent validation, performance ranged from sensitivity 89.3%/specificity 27.3% at a high-sensitivity threshold to sensitivity 28.6%/specificity 100% at a high-specificity threshold. Conclusion AI-derived cardiac volume from routine chest CT shows fair to good performance for identifying echocardiography-defined cardiomegaly with high measurement repeatability, supporting a potential role for automated cardiac volumetry as an objective, opportunistic biomarker.

AbstractIntroductionAcute myeloid leukemia (AML) remains one of the deadliest hematopoietic malignancies. A better understanding of the molecular biology governing AML may lead to improved risk stratification and facilitate the development of novel therapies. Proteins are responsible for much of the biology of cells. Several studies have examined the global proteome in bulk mononuclear cells (MNCs) from AML specimens, which are comprised a heterogenous population of cells at various stages of differentiation.MethodsGiven the potential impact of the nonleukemic cells on protein expression profiles, we applied an integrative proteogenomic approach utilizing next‐generation sequencing and mass spectrometry‐based proteomics to identify novel protein biomarkers in unsorted MNCs and viable leukemic blasts (VLBs) isolated from blood and bone marrow specimens obtained at the time of AML diagnosis.ResultsWe identified significant differences in protein expression between VLBs and MNCs. Subsequent studies (N = 27) focused on proteomic profiling of VLBs that identified novel candidate biomarkers associated with mutational genotypes and clinical outcome, some of which were recapitulated in an independent cohort of patients. Using mass spectrometry, we also detected mutated protein products, some of which were predicted via in silico analyses to be potential neoantigens amenable to adoptive immunotherapy. As previously described, analyses comparing transcript and protein expression showed an overall modest correlation between mRNA and protein dataset, but enriching for genes associated with mutations significantly improved the protein–RNA correlation.ConclusionTogether, the results provide insight into the biology of VLBs and demonstrate the gains derived from examining the proteome in addition to genome and transcriptome.

Objectives: To evaluate if dual-energy CT (DECT) pulmonary angiography (CTPA) can detect anemia with the aid of machine learning. Methods: Inclusion of 100 patients (mean age ± SD, 51.3 ± 14.8 years; male-to-female ratio, 42/58) who underwent DECT CTPA and hemoglobin (Hb) analysis within 24 h, including 50 cases with Hb below and 50 controls with Hb ≥ 12 g/dL. Blood pool attenuation was assessed on virtual noncontrast (VNC) images at eight locations. A classification model using extreme gradient-boosted trees was developed on a training set (n = 76) for differentiating cases from controls. The best model was evaluated in a separate test set (n = 24). Results: Blood pool attenuation was significantly lower in cases than controls (p-values < 0.01), except in the right atrium (p = 0.06). The machine learning model had sensitivity, specificity, and accuracy of 83%, 92%, and 88%, respectively. Measurements at the descending aorta had the highest relative importance among all features; a threshold of 43 HU yielded sensitivity, specificity, and accuracy of 68%, 76%, and 72%, respectively. Conclusion: VNC imaging and machine learning shows good diagnostic performance for detecting anemia on DECT CTPA.

Abstract Platinum-based DNA cross-linking agents are widely used anti-cancer drugs. Tumor resistance to platinum compounds is a major determinant of patient survival, including in high grade serous ovarian cancer (HGSOC). Remarkably, despite &gt;30 years of literature on platinum responses in human cancer, none of these findings is used clinically as a predictive biomarker to stratify patients for platinum resistance, nor exploited therapeutically to treat platinum-resistant disease. Thus, understanding mechanisms of platinum resistance is an urgent goal, both to identify predictive biomarkers of platinum response (to spare patients with platinum-resistant tumors futile platinum therapy) and to develop efficacious therapies for platinum-resistant disease. To better understand mechanisms underlying platinum resistance in HGSOC, we performed comprehensive, dynamic (+/-carboplatin), multiomic profiling of DNA, RNA, protein and post-translational modifications (phosphorylation, ubiquitination, acetylation) to identify the cellular networks that respond to platinum treatment and associate with platinum resistance in three HGSOC intra-patient cell line pairs (PEA1S/PEA2R, PEO1S/PEO4R, PEO14S/PEO23R). The cell line pairs were derived from ascites or pleural effusions (Langdon et al., 1988) from three patients both before (PEA1S, PEO1S, PEO14S) and after (PEA2R, PEO4R, PEO23R, respectively) their tumors became clinically platinum resistant (i.e., in vivo development of resistance). The molecular profiles revealed extensive responses to carboplatin and differential responses between platinum-sensitive and platinum-resistant cells. Higher oxidative phosphorylation and fatty acid oxidation (FAO) pathway expression were observed in the platinum-resistant cells, which was further validated in patient-derived xenograft (PDX) models. We show that pharmacologic inhibition or CRISPR knockout of CPT1A, which represents a rate limiting step of FAO, sensitize HGSOC cells to platinum. Thus, FAO, and CPT1A in particular, represent a candidate therapeutic target to overcome platinum resistance in HGSOC. Citation Format: Hong Wang, Dongqing Hugang, Shrabanti Chowdhury, Sara Savage, Richard Ivey, Jacob Kennedy, Jeffrey Whiteaker, Chenwei Lin, Xiaonan Hou, Catherine Huntoon, Uliana Voytovich, Zahra Shire, Qing Yu, Steven Gygi, Andrew Hoofnagle, Zachary Herbert, Anna Calinawan, Larry Karnitz, S. John Weroha, Scott Kaufmann, Bing Zhang, Pei Wang, Michael Birrer, Amanda Paulovich. Multiomic analysis identifies CPT1A and fatty acid oxidation as a potential therapeutic target in platinum-refractory high grade serous ovarian cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1426.

Abstract Although high-grade serous ovarian cancers (HGSOC) are highly chemosensitive with an 85% initial response rate to platinum-based chemotherapy, 15% of patients are “exceptional nonresponders,” with platinum-refractory tumors that remain stable or progress during treatment. Unfortunately, we have no predictive biomarkers to identify refractory patients up front, and they receive futile chemotherapy through which most patients become too ill to be eligible for clinical trials. Hence, no progress has been made in treating these deadly tumors. The goal of this study is to identify mechanisms of platinum refractoriness to: i) predict refractory HGSOCs up front and ii) identify potential new drug targets in refractory disease to point to desperately needed new therapeutic approaches. Of note, 80-90% of patients who are initially platinum responsive will relapse and develop platinum-resistant disease, and it is possible that findings in platinum-refractory tumors might also provide insights into platinum-resistant tumors. Our NCI Clinical Proteomic Tumor Analysis Consortium (CPTAC)-funded approach combines genomic and proteomic (“proteogenomic”) analyses of both preclinical models (0, 8, and 24 hours post-platinum exposure) and treatment-naïve human tumors. For preclinical models, we studied a well-characterized collection of patient-derived xenograft (PDX) models (10 sensitive, 10 refractory), as well as intrapatient HGSOC cell line pairs derived from patients before and after the development of platinum resistance. For the PDX models, proteogenomic profiling included RNASeq, WES, global proteomics, and phosphoproteomics at all 3 time points (0, 8, 24 hours). For cell line models (3 sensitive, 3 resistant), proteogenomic profiling was performed at all 3 timepoints (0, 8, 24 hours), and experiments were performed in complete biologic triplicate. Analyses included RNASeq, WES, global proteomics, phosphoproteomics, ubiquitin proteome, acetylated proteome, and pTyr. A large collection of 275 human HGSOCs (an approximate equal balance of platinum sensitive and refractory tumors) is currently undergoing proteomic profiling, and genomic profiles (WGS, RNASeq) will be performed on a subset. In parallel, we have performed a comprehensive review of 31 years of published work on platinum responses of human cancers, identifying ~700 genes implicated in the response and scoring each gene with respect to strength of the published evidence. Using a Bayesian approach, we are integrating the curated candidates from the literature with our empirical proteogenomic datasets to identify a candidate signature for detecting platinum-refractory disease prior to chemotherapy. We are also performing gene-regulatory network analysis to identify potential drivers of chemo response. NextGen, targeted, multiplex, multiple reaction monitoring mass spectrometry-based assays are being developed to quantify proteins in the signature for validation studies, using independent patient cohorts. This abstract is also being presented as Poster A62. Citation Format: Jacob J. Kennedy, Shrabanti Chowdhury, Sara R. Savage, Xiaonan Hou, Catherine J. Huntoon, Richard G. Ivey, Qing Yu, Chenwei Lin, Dongqing Huang, Lei Zhao, Uliana J. Voytovich, Regine M. Schoenherr, Zahra Shire, Steven J. Skates, Jeffrey R. Whiteaker, Andrew N. Hoofnagle, Samuel C. Mok, Bing Zhang, Larry M. Karnitz, S. John Weroha, Steven P. Gygi, Scott H. Kaufmann, Pei Wang, Michael J. Birrer, Amanda G. Paulovich. Proteogenomic approach to identify mechanisms of platinum refractoriness in high-grade serous ovarian cancers [abstract]. In: Proceedings of the AACR Special Conference on Advances in Ovarian Cancer Research; 2019 Sep 13-16, 2019; Atlanta, GA. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(13_Suppl):Abstract nr PR02.