Detection of myeloma-associated osteolytic bone lesions with energy-integrating and photon-counting detector CT.

BACKGROUND: A recent innovation in computed tomography (CT) imaging has been the introduction of photon-counting detector CT (PCD-CT) systems, which are able to register the number and the energy level of incoming x­ray photons and have smaller detector elements compared with conventional CT scanners that operate with energy-integrating detectors (EID-CT). OBJECTIVES: The study aimed to evaluate the potential benefits of a novel, non-CE certified PCD-CT in detecting myeloma-associated osteolytic bone lesions (OL) compared with a state-of-the-art EID-CT. MATERIALS AND METHODS: Nine patients with multiple myeloma stage III (according to Durie and Salmon) underwent magnetic resonance imaging (MRI), EID-CT, and PCD-CT of the lower lumbar spine and pelvis. The PCD-CT and EID-CT images of all myeloma lesions that were visible in clinical MRI scans were reviewed by three radiologists for corresponding OL. Additionally, the visualization of destructions to cancellous or cortical bone, and trabecular structures, was compared between PCD-CT and EID-CT. RESULTS: Readers detected 21% more OL in PCD-CT than in EID-CT images (138 vs. 109; p < 0.0001). The sensitivity advantage of PCD-CT in lesion detection increased with decreasing lesion size. The visualization quality of cancellous and cortical destructions as well as of trabecular structures was rated higher by all three readers in PCD-CT images (mean image quality improvements for PCD-CT over EID-CT were +0.45 for cancellous and +0.13 for cortical destructions). CONCLUSIONS: For myeloma-associated OL, PCD-CT demonstrated significantly higher sensitivity, especially with small size. Visualization of bone tissue and lesions was considered significantly better in PCD-CT than in EID-CT. This implies that PCD-CT scanners could potentially be used in the early detection of myeloma-associated bone lesions.

Selective haematological cancer eradication with preserved haematopoiesis.

Haematopoietic stem cell (HSC) transplantation (HSCT) is the only curative treatment for a broad range of haematological malignancies, but the standard of care relies on untargeted chemotherapies and limited possibilities to treat malignant cells after HSCT without affecting the transplanted healthy cells1. Antigen-specific cell-depleting therapies hold the promise of much more targeted elimination of diseased cells, as witnessed in the past decade by the revolution of clinical practice for B cell malignancies2. However, target selection is complex and limited to antigens expressed on subsets of haematopoietic cells, resulting in a fragmented therapy landscape with high development costs2-5. Here we demonstrate that an antibody-drug conjugate (ADC) targeting the pan-haematopoietic marker CD45 enables the antigen-specific depletion of the entire haematopoietic system, including HSCs. Pairing this ADC with the transplantation of human HSCs engineered to be shielded from the CD45-targeting ADC enables the selective eradication of leukaemic cells with preserved haematopoiesis. The combination of CD45-targeting ADCs and engineered HSCs creates an almost universal strategy to replace a diseased haematopoietic system, irrespective of disease aetiology or originating cell type. We propose that this approach could have broad implications beyond haematological malignancies.

Reproducible Radiomics Features from Multi-MRI-Scanner Test-Retest-Study: Influence on Performance and Generalizability of Models.

BACKGROUND: Radiomics models trained on data from one center typically show a decline of performance when applied to data from external centers, hindering their introduction into large-scale clinical practice. Current expert recommendations suggest to use only reproducible radiomics features isolated by multiscanner test-retest experiments, which might help to overcome the problem of limited generalizability to external data. PURPOSE: To evaluate the influence of using only a subset of robust radiomics features, defined in a prior in vivo multi-MRI-scanner test-retest-study, on the performance and generalizability of radiomics models. STUDY TYPE: Retrospective. POPULATION: Patients with monoclonal plasma cell disorders. Training set (117 MRIs from center 1); internal test set (42 MRIs from center 1); external test set (143 MRIs from center 2-8). FIELD STRENGTH/SEQUENCE: 1.5T and 3.0T; T1-weighted turbo spin echo. ASSESSMENT: The task for the radiomics models was to predict plasma cell infiltration, determined by bone marrow biopsy, noninvasively from MRI. Radiomics machine learning models, including linear regressor, support vector regressor (SVR), and random forest regressor (RFR), were trained on data from center 1, using either all radiomics features, or using only reproducible radiomics features. Models were tested on an internal (center 1) and a multicentric external data set (center 2-8). STATISTICAL TESTS: Pearson correlation coefficient r and mean absolute error (MAE) between predicted and actual plasma cell infiltration. Fisher's z-transformation, Wilcoxon signed-rank test, Wilcoxon rank-sum test; significance level P < 0.05. RESULTS: When using only reproducible features compared with all features, the performance of the SVR on the external test set significantly improved (r = 0.43 vs. r = 0.18 and MAE = 22.6 vs. MAE = 28.2). For the RFR, the performance on the external test set deteriorated when using only reproducible instead of all radiomics features (r = 0.33 vs. r = 0.44, P = 0.29 and MAE = 21.9 vs. MAE = 20.5, P = 0.10). CONCLUSION: Using only reproducible radiomics features improves the external performance of some, but not all machine learning models, and did not automatically lead to an improvement of the external performance of the overall best radiomics model. TECHNICAL EFFICACY: Stage 2.

From innate to instructed: A new look at perceptual decision-making.

Understanding how subjects perceive sensory stimuli in their environment and use this information to guide appropriate actions is a major challenge in neuroscience. To study perceptual decision-making in animals, researchers use tasks that either probe spontaneous responses to stimuli (often described as "naturalistic") or train animals to associate stimuli with experimenter-defined responses. Spontaneous decisions rely on animals' pre-existing knowledge, while trained tasks offer greater versatility, albeit often at the cost of extensive training. Here, we review emerging approaches to investigate perceptual decision-making using both spontaneous and trained behaviors, highlighting their strengths and limitations. Additionally, we propose how trained decision-making tasks could be improved to achieve faster learning and a more generalizable understanding of task rules.

Melanoma Clonal Heterogeneity Leads to Secondary Resistance after Adoptive Cell Therapy with Tumor-Infiltrating Lymphocytes.

Adoptive cell therapy (ACT) with tumor-infiltrating lymphocytes (TIL) is effective in patients with melanoma, although long-term responses seem restricted in patients who have complete remissions. Many patients develop secondary resistance to TIL-ACT but the involved mechanisms are unclear. In this study, we describe a case of secondary resistance to TIL-ACT possibly due to intratumoral heterogeneity and selection of a resistant tumor cell clone by the transferred T cells. To the best our knowledge, this is the first case of clonal selection of a pre-existing nondominant tumor cell clone; this report demonstrates the mechanism involved in secondary resistance to TIL-ACT that can potentially change current clinical practice because it advocates for T-cell collection from multiple tumor sites and analysis of tumor heterogeneity before treatment with TIL-ACT.

Lab-scale tubular LED UV reactor for continuous photocatalysis.

Photocatalytic water treatment is considered a promising technique to prevent micropollutants from entering the environment. However, no off-the-shelf UV reactors on lab scale are available to study new processes and photocatalysts. In this study, we present a tubular UV reactor equipped with 30 UV-LEDs, emitting UV light at 367 nm and a total radiant flux of 42 W. The UV reactor has an irradiated length of 300 mm and can host any transparent chemical reactor on the inside with a maximum diameter of 28 mm. The device is optimized for lab experiments with total dimensions of just 334 mm x 193 mm x 172 mm. Besides water treatment, a broad range of other photochemical and photocatalytic experiments can be performed with the reactor. Two identical UV reactors have been built and are successfully used for photocatalytic water treatment experiments. The degradation of methylene blue with TiO2 as photocatalyst was studied to validate the UV reactor. Furthermore, photocatalytic and hybrid processes were conducted in the UV reactor to degrade a broad range of pharmaceutical micropollutants.

TGF-ß specifies TFH versus TH17 cell fates in murine CD4+ T cells through c-Maf.

T follicular helper (TFH) cells are essential for effective antibody responses, but deciphering the intrinsic wiring of mouse TFH cells has long been hampered by the lack of a reliable protocol for their generation in vitro. We report that transforming growth factor-ß (TGF-ß) induces robust expression of TFH hallmark molecules CXCR5 and Bcl6 in activated mouse CD4+ T cells in vitro. TGF-ß-induced mouse CXCR5+ TFH cells are phenotypically, transcriptionally, and functionally similar to in vivo-generated TFH cells and provide critical help to B cells. The study further reveals that TGF-ß-induced CXCR5 expression is independent of Bcl6 but requires the transcription factor c-Maf. Classical TGF-ß-containing T helper 17 (TH17)-inducing conditions also yield separate CXCR5+ and IL-17A-producing cells, highlighting shared and distinct cell fate trajectories of TFH and TH17 cells. We demonstrate that excess IL-2 in high-density T cell cultures interferes with the TGF-ß-induced TFH cell program, that TFH and TH17 cells share a common developmental stage, and that c-Maf acts as a switch factor for TFH versus TH17 cell fates in TGF-ß-rich environments in vitro and in vivo.

T helper cells exhibit a dynamic and reversible 3'-UTR landscape.

3' untranslated regions (3' UTRs) are critical elements of messenger RNAs, as they contain binding sites for RNA-binding proteins (RBPs) and microRNAs that affect various aspects of the RNA life cycle including transcript stability and cellular localization. In response to T cell receptor activation, T cells undergo massive expansion during the effector phase of the immune response and dynamically modify their 3' UTRs. Whether this serves to directly regulate the abundance of specific mRNAs or is a secondary effect of proliferation remains unclear. To study 3'-UTR dynamics in T helper cells, we investigated division-dependent alternative polyadenylation (APA). In addition, we generated 3' end UTR sequencing data from naive, activated, memory, and regulatory CD4+ T cells. 3'-UTR length changes were estimated using a nonnegative matrix factorization approach and were compared with those inferred from long-read PacBio sequencing. We found that APA events were transient and reverted after effector phase expansion. Using an orthogonal bulk RNA-seq data set, we did not find evidence of APA association with differential gene expression or transcript usage, indicating that APA has only a marginal effect on transcript abundance. 3'-UTR sequence analysis revealed conserved binding sites for T cell-relevant microRNAs and RBPs in the alternative 3' UTRs. These results indicate that poly(A) site usage could play an important role in the control of cell fate decisions and homeostasis.

Tumor classification of gastrointestinal liver metastases using CT-based radiomics and deep learning.

OBJECTIVES: The goal of this study is to demonstrate the performance of radiomics and CNN-based classifiers in determining the primary origin of gastrointestinal liver metastases for visually indistinguishable lesions. METHODS: In this retrospective, IRB-approved study, 31 pancreatic cancer patients with 861 lesions (median age [IQR]: 65.39 [56.87, 75.08], 48.4% male) and 47 colorectal cancer patients with 435 lesions (median age [IQR]: 65.79 [56.99, 74.62], 63.8% male) were enrolled. A pretrained nnU-Net performed automated segmentation of 1296 liver lesions. Radiomics features for each lesion were extracted using pyradiomics. The performance of several radiomics-based machine-learning classifiers was investigated for the lesions and compared to an image-based deep-learning approach using a DenseNet-121. The performance was evaluated by AUC/ROC analysis. RESULTS: The radiomics-based K-nearest neighbor classifier showed the best performance on an independent test set with AUC values of 0.87 and an accuracy of 0.67. In comparison, the image-based DenseNet-121-classifier reached an AUC of 0.80 and an accuracy of 0.83. CONCLUSIONS: CT-based radiomics and deep learning can distinguish the etiology of liver metastases from gastrointestinal primary tumors. Compared to deep learning, radiomics based models showed a varying generalizability in distinguishing liver metastases from colorectal cancer and pancreatic adenocarcinoma.

Leveraging microRNAs for cellular therapy.

Owing to Karl Landsteiner's discovery of blood groups, blood transfusions became safe cellular therapies in the early 1900s. Since then, cellular therapy made great advances from transfusions with unmodified cells to today's commercially available chimeric antigen receptor (CAR) T cells requiring complex manufacturing. Modern cellular therapy products can be improved using basic knowledge of cell biology and molecular genetics. Emerging genome engineering tools are becoming ever more versatile and precise and thus catalyze rapid progress towards programmable therapeutic cells that compute input and respond with defined output. Despite a large body of literature describing important functions of non-coding RNAs including microRNAs (miRNAs), the vast majority of cell engineering efforts focuses on proteins. However, miRNAs form an important layer of posttranscriptional regulation of gene expression. Here, we highlight examples of how miRNAs can successfully be incorporated into engineered cellular therapies.

Epitope-engineered human hematopoietic stem cells are shielded from CD123-targeted immunotherapy.

Targeted eradication of transformed or otherwise dysregulated cells using monoclonal antibodies (mAb), antibody-drug conjugates (ADC), T cell engagers (TCE), or chimeric antigen receptor (CAR) cells is very effective for hematologic diseases. Unlike the breakthrough progress achieved for B cell malignancies, there is a pressing need to find suitable antigens for myeloid malignancies. CD123, the interleukin-3 (IL-3) receptor alpha-chain, is highly expressed in various hematological malignancies, including acute myeloid leukemia (AML). However, shared CD123 expression on healthy hematopoietic stem and progenitor cells (HSPCs) bears the risk for myelotoxicity. We demonstrate that epitope-engineered HSPCs were shielded from CD123-targeted immunotherapy but remained functional, while CD123-deficient HSPCs displayed a competitive disadvantage. Transplantation of genome-edited HSPCs could enable tumor-selective targeted immunotherapy while rebuilding a fully functional hematopoietic system. We envision that this approach is broadly applicable to other targets and cells, could render hitherto undruggable targets accessible to immunotherapy, and will allow continued posttransplant therapy, for instance, to treat minimal residual disease (MRD).

Activity-based profiling of cullin-RING E3 networks by conformation-specific probes.

The cullin-RING ubiquitin ligase (CRL) network comprises over 300 unique complexes that switch from inactive to activated conformations upon site-specific cullin modification by the ubiquitin-like protein NEDD8. Assessing cellular repertoires of activated CRL complexes is critical for understanding eukaryotic regulation. However, probes surveying networks controlled by site-specific ubiquitin-like protein modifications are lacking. We developed a synthetic antibody recognizing the active conformation of NEDD8-linked cullins. Implementing the probe to profile cellular networks of activated CUL1-, CUL2-, CUL3- and CUL4-containing E3s revealed the complexes responding to stimuli. Profiling several cell types showed their baseline neddylated CRL repertoires vary, and prime efficiency of targeted protein degradation. Our probe also unveiled differential rewiring of CRL networks across distinct primary cell activation pathways. Thus, conformation-specific probes can permit nonenzymatic activity-based profiling across a system of numerous multiprotein complexes, which in the case of neddylated CRLs reveals widespread regulation and could facilitate the development of degrader drugs.

Periaortic adipose radiomics texture features associated with increased coronary calcium score-first results on a photon-counting-CT.

BACKGROUND: Cardiovascular diseases remain the world's primary cause of death. The identification and treatment of patients at risk of cardiovascular events thus are as important as ever. Adipose tissue is a classic risk factor for cardiovascular diseases, has been linked to systemic inflammation, and is suspected to contribute to vascular calcification. To further investigate this issue, the use of texture analysis of adipose tissue using radiomics features could prove a feasible option. METHODS: In this retrospective single-center study, 55 patients (mean age 56, 34 male, 21 female) were scanned on a first-generation photon-counting CT. On axial unenhanced images, periaortic adipose tissue surrounding the thoracic descending aorta was segmented manually. For feature extraction, patients were divided into three groups, depending on coronary artery calcification (Agatston Score 0, Agatston Score 1-99, Agatston Score ≥ 100). 106 features were extracted using pyradiomics. R statistics was used for statistical analysis, calculating mean and standard deviation with Pearson correlation coefficient for feature correlation. Random Forest classification was carried out for feature selection and Boxplots and heatmaps were used for visualization. Additionally, monovariable logistic regression predicting an Agatston Score > 0 was performed, selected features were tested for multicollinearity and a 10-fold cross-validation investigated the stability of the leading feature. RESULTS: Two higher-order radiomics features, namely "glcm_ClusterProminence" and "glcm_ClusterTendency" were found to differ between patients without coronary artery calcification and those with coronary artery calcification (Agatston Score ≥ 100) through Random Forest classification. As the leading differentiating feature "glcm_ClusterProminence" was identified. CONCLUSION: Changes in periaortic adipose tissue texture seem to correlate with coronary artery calcium score, supporting a possible influence of inflammatory or fibrotic activity in perivascular adipose tissue. Radiomics features may potentially aid as corresponding biomarkers in the future.

Proteomics of immune cells from liver tumors reveals immunotherapy targets.

Elucidating the mechanisms by which immune cells become dysfunctional in tumors is critical to developing next-generation immunotherapies. We profiled proteomes of cancer tissue as well as monocyte/macrophages, CD4+ and CD8+ T cells, and NK cells isolated from tumors, liver, and blood of 48 patients with hepatocellular carcinoma. We found that tumor macrophages induce the sphingosine-1-phospate-degrading enzyme SGPL1, which dampened their inflammatory phenotype and anti-tumor function in vivo. We further discovered that the signaling scaffold protein AFAP1L2, typically only found in activated NK cells, is also upregulated in chronically stimulated CD8+ T cells in tumors. Ablation of AFAP1L2 in CD8+ T cells increased their viability upon repeated stimulation and enhanced their anti-tumor activity synergistically with PD-L1 blockade in mouse models. Our data reveal new targets for immunotherapy and provide a resource on immune cell proteomes in liver cancer.

Prediction of Bone Marrow Biopsy Results From MRI in Multiple Myeloma Patients Using Deep Learning and Radiomics.

OBJECTIVES: In multiple myeloma and its precursor stages, plasma cell infiltration (PCI) and cytogenetic aberrations are important for staging, risk stratification, and response assessment. However, invasive bone marrow (BM) biopsies cannot be performed frequently and multifocally to assess the spatially heterogenous tumor tissue. Therefore, the goal of this study was to establish an automated framework to predict local BM biopsy results from magnetic resonance imaging (MRI). MATERIALS AND METHODS: This retrospective multicentric study used data from center 1 for algorithm training and internal testing, and data from center 2 to 8 for external testing. An nnU-Net was trained for automated segmentation of pelvic BM from T1-weighted whole-body MRI. Radiomics features were extracted from these segmentations, and random forest models were trained to predict PCI and the presence or absence of cytogenetic aberrations. Pearson correlation coefficient and the area under the receiver operating characteristic were used to evaluate the prediction performance for PCI and cytogenetic aberrations, respectively. RESULTS: A total of 672 MRIs from 512 patients (median age, 61 years; interquartile range, 53-67 years; 307 men) from 8 centers and 370 corresponding BM biopsies were included. The predicted PCI from the best model was significantly correlated ( P ≤ 0.01) to the actual PCI from biopsy in all internal and external test sets (internal test set: r = 0.71 [0.51, 0.83]; center 2, high-quality test set: r = 0.45 [0.12, 0.69]; center 2, other test set: r = 0.30 [0.07, 0.49]; multicenter test set: r = 0.57 [0.30, 0.76]). The areas under the receiver operating characteristic of the prediction models for the different cytogenetic aberrations ranged from 0.57 to 0.76 for the internal test set, but no model generalized well to all 3 external test sets. CONCLUSIONS: The automated image analysis framework established in this study allows for noninvasive prediction of a surrogate parameter for PCI, which is significantly correlated to the actual PCI from BM biopsy.

Systemwide disassembly and assembly of SCF ubiquitin ligase complexes.

Cells respond to environmental cues by remodeling their inventories of multiprotein complexes. Cellular repertoires of SCF (SKP1-CUL1-F box protein) ubiquitin ligase complexes, which mediate much protein degradation, require CAND1 to distribute the limiting CUL1 subunit across the family of ∼70 different F box proteins. Yet, how a single factor coordinately assembles numerous distinct multiprotein complexes remains unknown. We obtained cryo-EM structures of CAND1-bound SCF complexes in multiple states and correlated mutational effects on structures, biochemistry, and cellular assays. The data suggest that CAND1 clasps idling catalytic domains of an inactive SCF, rolls around, and allosterically rocks and destabilizes the SCF. New SCF production proceeds in reverse, through SKP1-F box allosterically destabilizing CAND1. The CAND1-SCF conformational ensemble recycles CUL1 from inactive complexes, fueling mixing and matching of SCF parts for E3 activation in response to substrate availability. Our data reveal biogenesis of a predominant family of E3 ligases, and the molecular basis for systemwide multiprotein complex assembly.

Deletion of SNX9 alleviates CD8 T cell exhaustion for effective cellular cancer immunotherapy.

Tumor-specific T cells are frequently exhausted by chronic antigenic stimulation. We here report on a human antigen-specific ex vivo model to explore new therapeutic options for T cell immunotherapies. T cells generated with this model resemble tumor-infiltrating exhausted T cells on a phenotypic and transcriptional level. Using a targeted pooled CRISPR-Cas9 screen and individual gene knockout validation experiments, we uncover sorting nexin-9 (SNX9) as a mediator of T cell exhaustion. Upon TCR/CD28 stimulation, deletion of SNX9 in CD8 T cells decreases PLCγ1, Ca2+, and NFATc2-mediated T cell signaling and reduces expression of NR4A1/3 and TOX. SNX9 knockout enhances memory differentiation and IFNγ secretion of adoptively transferred T cells and results in improved anti-tumor efficacy of human chimeric antigen receptor T cells in vivo. Our findings highlight that targeting SNX9 is a strategy to prevent T cell exhaustion and enhance anti-tumor immunity.

Phantom-based radiomics feature test-retest stability analysis on photon-counting detector CT.

OBJECTIVES: Radiomics image data analysis offers promising approaches in research but has not been implemented in clinical practice yet, partly due to the instability of many parameters. The aim of this study is to evaluate the stability of radiomics analysis on phantom scans with photon-counting detector CT (PCCT). METHODS: Photon-counting CT scans of organic phantoms consisting of 4 apples, kiwis, limes, and onions each were performed at 10 mAs, 50 mAs, and 100 mAs with 120-kV tube current. The phantoms were segmented semi-automatically and original radiomics parameters were extracted. This was followed by statistical analysis including concordance correlation coefficients (CCC), intraclass correlation coefficients (ICC), as well as random forest (RF) analysis, and cluster analysis to determine the stable and important parameters. RESULTS: Seventy-three of the 104 (70%) extracted features showed excellent stability with a CCC value > 0.9 when compared in a test and retest analysis, and 68 features (65.4%) were stable compared to the original in a rescan after repositioning. Between the test scans with different mAs values, 78 (75%) features were rated with excellent stability. Eight radiomics features were identified that had an ICC value greater than 0.75 in at least 3 of 4 groups when comparing the different phantoms in a phantom group. In addition, the RF analysis identified many features that are important for distinguishing the phantom groups. CONCLUSION: Radiomics analysis using PCCT data provides high feature stability on organic phantoms, which may facilitate the implementation of radiomics analysis likewise in clinical routine. KEY POINTS: • Radiomics analysis using photon-counting computed tomography provides high feature stability. • Photon-counting computed tomography may pave the way for implementation of radiomics analysis in clinical routine.