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BACKGROUND: The PACIFIC trial established consolidative durvalumab after concurrent chemoradiation as standard-of-care in patients with stage III or unresectable non-small cell lung cancer (NSCLC). Black patients, however, comprised just 2% (n = 14) of randomized patients in this trial, warranting real-world evaluation of the PACIFIC regimen in these patients. METHODS: This single-institution, multi-site study included 105 patients with unresectable stage II/III NSCLC treated with concurrent chemoradiation followed by durvalumab between 2017 and 2021. Overall survival (OS), progression-free survival (PFS), and grade ≥3 pneumonitis-free survival (PNFS) were compared between Black and non-Black patients using Kaplan-Meier and Cox regression analyses. RESULTS: A total of 105 patients with a median follow-up of 22.8 months (interquartile range, 11.3-37.3 months) were identified for analysis, including 57 Black (54.3%) and 48 (45.7%) non-Black patients. The mean radiation prescription dose was higher among Black patients (61.5 ± 2.9 Gy vs. 60.5 ± 1.9 Gy; p = .031), but other treatment characteristics were balanced between groups. The median OS (not-reached vs. 39.7 months; p = .379) and PFS (31.6 months vs. 19.3 months; p = .332) were not statistically different between groups. Eight (14.0%) Black patients discontinued durvalumab due to toxicity compared to 13 (27.1%) non-Black patients (p = .096). The grade ≥3 pneumonitis rate was similar between Black and non-Black patients (12.3% vs. 12.5%; p = .973), and there was no significant difference in time to grade ≥3 PNFS (p = .904). Three (5.3%) Black patients and one (2.1%) non-Black patient developed grade 5 pneumonitis. CONCLUSIONS: The efficacy and tolerability of consolidative durvalumab after chemoradiation appears to be comparable between Black and non-Black patients.
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Carcinoma Pulmonar de Células não Pequenas , Neoplasias Pulmonares , Pneumonia , Humanos , Carcinoma Pulmonar de Células não Pequenas/tratamento farmacológico , Carcinoma Pulmonar de Células não Pequenas/radioterapia , Neoplasias Pulmonares/tratamento farmacológico , Neoplasias Pulmonares/radioterapia , Quimiorradioterapia/efeitos adversosRESUMO
Tumor metabolism has emerged as a hallmark of cancer and is involved in carcinogenesis and tumor growth. Reprogramming of tumor metabolism is necessary for cancer cells to sustain high proliferation rates and enhanced demands for nutrients. Recent studies suggest that metabolic plasticity in cancer cells can decrease the efficacy of anticancer therapies by enhancing antioxidant defenses and DNA repair mechanisms. Studying radiation-induced metabolic changes will lead to a better understanding of radiation response mechanisms as well as the identification of new therapeutic targets, but there are few robust studies characterizing the metabolic changes induced by radiation therapy in cancer. In this review, we will highlight studies that provide information on the metabolic changes induced by radiation and oxidative stress in cancer cells and the associated underlying mechanisms.
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Neoplasias , Carcinogênese , Reparo do DNA , Humanos , Neoplasias/genética , Neoplasias/metabolismo , Neoplasias/radioterapia , Estresse OxidativoRESUMO
Hepatocellular carcinoma (HCC) is the second most common cause of cancer-related death. Non-alcoholic fatty liver disease (NAFLD) affects a large proportion of the US population and is considered to be a metabolic predisposition to liver cancer. However, the role of adaptive immune responses in NAFLD-promoted HCC is largely unknown. Here we show, in mouse models and human samples, that dysregulation of lipid metabolism in NAFLD causes a selective loss of intrahepatic CD4(+) but not CD8(+) T lymphocytes, leading to accelerated hepatocarcinogenesis. We also demonstrate that CD4(+) T lymphocytes have greater mitochondrial mass than CD8(+) T lymphocytes and generate higher levels of mitochondrially derived reactive oxygen species (ROS). Disruption of mitochondrial function by linoleic acid, a fatty acid accumulated in NAFLD, causes more oxidative damage than other free fatty acids such as palmitic acid, and mediates selective loss of intrahepatic CD4(+) T lymphocytes. In vivo blockade of ROS reversed NAFLD-induced hepatic CD4(+) T lymphocyte decrease and delayed NAFLD-promoted HCC. Our results provide an unexpected link between lipid dysregulation and impaired anti-tumour surveillance.
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Linfócitos T CD4-Positivos/patologia , Carcinogênese , Carcinoma Hepatocelular/imunologia , Carcinoma Hepatocelular/patologia , Neoplasias Hepáticas/imunologia , Neoplasias Hepáticas/patologia , Hepatopatia Gordurosa não Alcoólica/imunologia , Animais , Linfócitos T CD4-Positivos/imunologia , Linfócitos T CD4-Positivos/metabolismo , Linfócitos T CD8-Positivos/imunologia , Linfócitos T CD8-Positivos/patologia , Carcinogênese/imunologia , Carcinogênese/patologia , Carcinoma Hepatocelular/metabolismo , Estudos de Casos e Controles , Colina/metabolismo , Dieta , Modelos Animais de Doenças , Genes myc , Hepatócitos/metabolismo , Hepatócitos/patologia , Humanos , Ácido Linoleico/metabolismo , Metabolismo dos Lipídeos , Fígado/imunologia , Fígado/patologia , Neoplasias Hepáticas/metabolismo , Masculino , Metionina/deficiência , Camundongos , Camundongos Endogâmicos C57BL , Mitocôndrias/metabolismo , Mitocôndrias/patologia , Hepatopatia Gordurosa não Alcoólica/metabolismo , Hepatopatia Gordurosa não Alcoólica/patologia , Estresse Oxidativo , Espécies Reativas de Oxigênio/metabolismoRESUMO
Isocitrate dehydrogenase 1 (IDH1) mutations that generate the oncometabolite 2-hydroxyglutarate (2-HG) from α-ketoglutarate (α-KG) have been identified in many types of tumors and are an important prognostic factor in gliomas. 2-HG production can be determined by hyperpolarized carbon-13 magnetic resonance spectroscopy (HP-13 C-MRS) using [1-13 C]-α-KG as a probe, but peak contamination from naturally occurring [5-13 C]-α-KG overlaps with the [1-13 C]-2-HG peak. Via a newly developed oxidative-Stetter reaction, [1-13 C-5-12 C]-α-KG was synthesized. α-KG metabolism was measured via HP-13 C-MRS using [1-13 C-5-12 C]-α-KG as a probe. [1-13 C-5-12 C]-α-KG was synthesized in high yields, and successfully eliminated the signal from C5 of α-KG in the HP-13 C-MRS spectra. In HCT116 IDH1 R132H cells, [1-13 C-5-12 C]-α-KG allowed for unimpeded detection of [1-13 C]-2-HG. 12 C-enrichment represents a novel method to circumvent spectral overlap, and [1-13 C-5-12 C]-α-KG shows promise as a probe to study IDH1 mutant tumors and α-KG metabolism.
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Espectroscopia de Ressonância Magnética Nuclear de Carbono-13 , Glutaratos/análise , Ácidos Cetoglutáricos/metabolismo , Células HCT116 , HumanosRESUMO
Small redox active molecules such as reactive nitrogen and oxygen species and hydrogen sulfide have emerged as important biological mediators that are involved in various physiological and pathophysiological processes. Advancement in understanding of cellular mechanisms that tightly regulate both generation and reactivity of these molecules is central to improved management of various disease states including cancer and cardiovascular dysfunction. Imbalance in the production of redox active molecules can lead to damage of critical cellular components such as cell membranes, proteins and DNA and thus may trigger the onset of disease. These small inorganic molecules react independently as well as in a concerted manner to mediate physiological responses. This review provides a general overview of the redox biology of these key molecules, their diverse chemistry relevant to physiological processes and their interrelated nature in cellular signaling.
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Nitric oxide (NO) synthase 2 (NOS2), a major inflammatory protein, modulates disease progression via NO in a number of pathologies, including cancer. The role of NOS2-derived NO is not only flux-dependent, which is higher in mouse vs human cells, but also varies based on spatial and temporal distribution both within tumor cells and in the tumor microenvironment. NO donors have been utilized to mimic NO flux conditions and to investigate the effects of varied NO concentrations. As a wide range of effects mediated by NO and other nitrogen oxides such as nitroxyl (HNO) have been elucidated, multiple NO- and HNO-releasing compounds have been developed as potential therapeutics, including as tumor modulators. One of the challenges is to determine differences in biomarker expression from extracellular vs intracellular generation of NO or HNO. Taking advantage of new NO and HNO releasing agents, we have characterized the gene expression profile of estrogen receptor-negative human breast cancer (MDA-MB-231) cells following exposure to aspirin, the NO donor DEA/NO, the HNO donor IPA/NO andtheir intracellularly-activated prodrug conjugates DEA/NO-aspirin and IPA/NO-aspirin. Comparison of the gene expression profiles demonstrated that several genes were uniquely expressed with respect to NO or HNO, such as miR-21, HSP70, cystathionine γ-lyase and IL24. These findings provide insight into targets and pathways that could be therapeutically exploited by the redox related species NO and HNO.
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Regulação Neoplásica da Expressão Gênica/efeitos dos fármacos , Doadores de Óxido Nítrico/farmacologia , Óxido Nítrico/farmacologia , Animais , Neoplasias da Mama/genética , Neoplasias da Mama/terapia , Linhagem Celular Tumoral , Resistencia a Medicamentos Antineoplásicos/efeitos dos fármacos , Feminino , Perfilação da Expressão Gênica , Humanos , Camundongos , Óxido Nítrico Sintase/metabolismo , Transdução de SinaisRESUMO
BACKGROUND: An automated, accurate, and efficient lung four-dimensional computed tomography (4DCT) image registration method is clinically important to quantify respiratory motion for optimal motion management. PURPOSE: The purpose of this work is to develop a weakly supervised deep learning method for 4DCT lung deformable image registration (DIR). METHODS: The landmark-driven cycle network is proposed as a deep learning platform that performs DIR of individual phase datasets in a simulation 4DCT. This proposed network comprises a generator and a discriminator. The generator accepts moving and target CTs as input and outputs the deformation vector fields (DVFs) to match the two CTs. It is optimized during both forward and backward paths to enhance the bi-directionality of DVF generation. Further, the landmarks are used to weakly supervise the generator network. Landmark-driven loss is used to guide the generator's training. The discriminator then judges the realism of the deformed CT to provide extra DVF regularization. RESULTS: We performed four-fold cross-validation on 10 4DCT datasets from the public DIR-Lab dataset and a hold-out test on our clinic dataset, which included 50 4DCT datasets. The DIR-Lab dataset was used to evaluate the performance of the proposed method against other methods in the literature by calculating the DIR-Lab Target Registration Error (TRE). The proposed method outperformed other deep learning-based methods on the DIR-Lab datasets in terms of TRE. Bi-directional and landmark-driven loss were shown to be effective for obtaining high registration accuracy. The mean and standard deviation of TRE for the DIR-Lab datasets was 1.20 ± 0.72 mm and the mean absolute error (MAE) and structural similarity index (SSIM) for our datasets were 32.1 ± 11.6 HU and 0.979 ± 0.011, respectively. CONCLUSION: The landmark-driven cycle network has been validated and tested for automatic deformable image registration of patients' lung 4DCTs with results comparable to or better than competing methods.
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Tomografia Computadorizada Quadridimensional , Processamento de Imagem Assistida por Computador , Humanos , Processamento de Imagem Assistida por Computador/métodos , Pulmão/diagnóstico por imagem , Simulação por Computador , Movimento (Física) , AlgoritmosRESUMO
BACKGROUND: Although cone beam computed tomography (CBCT) has lower resolution compared to planning CTs (pCT), its lower dose, higher high-contrast resolution, and shorter scanning time support its widespread use in clinical applications, especially in ensuring accurate patient positioning during the image-guided radiation therapy (IGRT) process. PURPOSE: While CBCT is critical to IGRT, CBCT image quality can be compromised by severe stripe and scattering artifacts. Tumor movement secondary to respiratory motion also decreases CBCT resolution. In order to improve the image quality of CBCT, we propose a Lung Diffusion Model (L-DM) framework. METHODS: Our proposed algorithm is based on a conditional diffusion model trained on pCT and deformed CBCT (dCBCT) image pairs to synthesize lung CT images from dCBCT images and benefit CBCT-based radiotherapy. dCBCT images were used as the constraint for the L-DM. The image quality and Hounsfield unit (HU) values of the synthetic CTs (sCT) images generated by the proposed L-DM were compared to three selected mainstream generation models. RESULTS: We verified our model in both an institutional lung cancer dataset and a selected public dataset. Our L-DM showed significant improvement in the four metrics of mean absolute error (MAE), peak signal-to-noise ratio (PSNR), normalized cross-correlation (NCC), and structural similarity index measure (SSIM). In our institutional dataset, our proposed L-DM decreased the MAE from 101.47 to 37.87 HU and increased the PSNR from 24.97 to 29.89 dB, the NCC from 0.81 to 0.97, and the SSIM from 0.80 to 0.93. In the public dataset, our proposed L-DM decreased the MAE from 173.65 to 58.95 HU, while increasing the PSNR, NCC, and SSIM from 13.07 to 24.05 dB, 0.68 to 0.94, and 0.41 to 0.88, respectively. CONCLUSIONS: The proposed L-DM significantly improved sCT image quality compared to the pre-correction CBCT and three mainstream generative models. Our model can benefit CBCT-based IGRT and other potential clinical applications as it increases the HU accuracy and decreases the artifacts from input CBCT images.
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BACKGROUND: Cone-beam computed tomography (CBCT) scans, performed fractionally (e.g., daily or weekly), are widely utilized for patient alignment in the image-guided radiotherapy (IGRT) process, thereby making it a potential imaging modality for the implementation of adaptive radiotherapy (ART) protocols. Nonetheless, significant artifacts and incorrect Hounsfield unit (HU) values hinder their application in quantitative tasks such as target and organ segmentations and dose calculation. Therefore, acquiring CT-quality images from the CBCT scans is essential to implement online ART in clinical settings. PURPOSE: This work aims to develop an unsupervised learning method using the patient-specific diffusion model for CBCT-based synthetic CT (sCT) generation to improve the image quality of CBCT. METHODS: The proposed method is in an unsupervised framework that utilizes a patient-specific score-based model as the image prior alongside a customized total variation (TV) regularization to enforce coherence across different transverse slices. The score-based model is unconditionally trained using the same patient's planning CT (pCT) images to characterize the manifold of CT-quality images and capture the unique anatomical information of the specific patient. The efficacy of the proposed method was assessed on images from anatomical sites including head and neck (H&N) cancer, pancreatic cancer, and lung cancer. The performance of the proposed CBCT correction method was evaluated using quantitative metrics including mean absolute error (MAE), peak signal-to-noise ratio (PSNR), and normalized cross-correlation (NCC). Additionally, the proposed algorithm was benchmarked against two other unsupervised diffusion model-based CBCT correction algorithms.
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Objectives. In this work, we proposed a deep-learning segmentation algorithm for cardiac magnetic resonance imaging to aid in contouring of the left ventricle, right ventricle, and Myocardium (Myo).Approach.We proposed a shifted window multilayer perceptron (Swin-MLP) mixer network which is built upon a 3D U-shaped symmetric encoder-decoder structure. We evaluated our proposed network using public data from 100 individuals. The network performance was quantitatively evaluated using 3D volume similarity between the ground truth contours and the predictions using Dice score coefficient, sensitivity, and precision as well as 2D surface similarity using Hausdorff distance (HD), mean surface distance (MSD) and residual mean square distance (RMSD). We benchmarked the performance against two other current leading edge networks known as Dynamic UNet and Swin-UNetr on the same public dataset.Results.The proposed network achieved the following volume similarity metrics when averaged over three cardiac segments: Dice = 0.952 ± 0.017, precision = 0.948 ± 0.016, sensitivity = 0.956 ± 0.022. The average surface similarities were HD = 1.521 ± 0.121 mm, MSD = 0.266 ± 0.075 mm, and RMSD = 0.668 ± 0.288 mm. The network shows statistically significant improvement in comparison to the Dynamic UNet and Swin-UNetr algorithms for most volumetric and surface metrics withp-value less than 0.05. Overall, the proposed Swin-MLP mixer network demonstrates better or comparable performance than competing methods.Significance.The proposed Swin-MLP mixer network demonstrates more accurate segmentation performance compared to current leading edge methods. This robust method demonstrates the potential to streamline clinical workflows for multiple applications.
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Coração , Processamento de Imagem Assistida por Computador , Imageamento por Ressonância Magnética , Humanos , Processamento de Imagem Assistida por Computador/métodos , Coração/diagnóstico por imagem , Redes Neurais de Computação , Aprendizado Profundo , AlgoritmosRESUMO
Purpose: Emerging data have illuminated the impact of effective radiation dose to immune cells (EDIC) on outcomes in patients with locally advanced, unresectable non-small cell lung cancer (NSCLC) treated with intensity-modulated radiotherapy (IMRT). Hypothesizing that intensity-modulated proton therapy (IMPT) may reduce EDIC versus IMRT, we conducted a dosimetric analysis of patients treated at our institution. Materials and Methods: Data were retrospectively collected for 12 patients with locally advanced, unresectable NSCLC diagnosed between 2019 and 2021 who had physician-approved IMRT and IMPT plans. Data to calculate EDIC from both Jin et al (PMID: 34944813) and Ladbury et al's (PMID: 31175902) models were abstracted. Paired t tests were utilized to compare the difference in mean EDIC between IMPT and IMRT plans. Results: IMPT decreased EDIC for 11 of 12 patients (91.7%). The mean EDIC per the Jin model was significantly lower with IMPT than IMRT (3.04 GyE vs 4.99 Gy, P < .001). Similarly, the mean EDIC per the Ladbury model was significantly lower with IMPT than IMRT (4.50 GyE vs 7.60 Gy, P < .002). Modeled 2-year overall survival was significantly longer with IMPT than IMRT (median 71% vs 63%; P = .03). Conclusion: IMPT offers a statistically significant reduction in EDIC compared to IMRT. Given the emergence of EDIC as a modifiable prognostic factor in treatment planning, our dosimetric study highlights a potential role for IMPT to address an unmet need in improving oncologic outcomes in patients with locoregionally advanced NSCLC.
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Radiotherapy (RT) and anti-PD-L1 synergize to enhance local and distant (abscopal) tumor control. However, clinical results in humans have been variable. With the goal of improving clinical outcomes, we investigated the underlying synergistic mechanism focusing on a CD8+ PD-1+ Tcf-1+ stem-like T cell subset in the tumor-draining lymph node (TdLN). Using murine melanoma models, we found that RT + anti-PD-L1 induces a novel differentiation program in the TdLN stem-like population which leads to their expansion and differentiation into effector cells within the tumor. Our data indicate that optimal synergy between RT + anti-PD-L1 is dependent on the TdLN stem-like T cell population as either blockade of TdLN egress or specific stem-like T cell depletion reduced tumor control. Together, these data demonstrate a multistep stimulation of stem-like T cells following combination therapy which is initiated in the TdLN and completed in the tumor.
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PURPOSE: The long acquisition time of CBCT discourages repeat verification imaging, therefore increasing treatment uncertainty. In this study, we present a fast volumetric imaging method for lung cancer radiation therapy using an orthogonal 2D kV/MV image pair. METHODS: The proposed model is a combination of 2D and 3D networks. The proposed model consists of five major parts: (1) kV and MV feature extractors are used to extract deep features from the perpendicular kV and MV projections. (2) The feature-matching step is used to re-align the feature maps to their projection angle in a Cartesian coordinate system. By using a residual module, the feature map can focus more on the difference between the estimated and ground truth images. (3) In addition, the feature map is downsized to include more global semantic information for the 3D estimation, which is useful to reduce inhomogeneity. By using convolution-based reweighting, the model is able to further increase the uniformity of image. (4) To reduce the blurry noise of generated 3D volume, the Laplacian latent space loss calculated via the feature map that is extracted via specifically-learned Gaussian kernel is used to supervise the network. (5) Finally, the 3D volume is derived from the trained model. We conducted a proof-of-concept study using 50 patients with lung cancer. An orthogonal kV/MV pair was generated by ray tracing through CT of each phase in a 4D CT scan. Orthogonal kV/MV pairs from nine respiratory phases were used to train this patient-specific model while the kV/MV pair of the remaining phase was held for model testing. RESULTS: The results are based on simulation data and phantom results from a real Linac system. The mean absolute error (MAE) values achieved by our method were 57.5 HU and 77.4 HU within body and tumor region-of-interest (ROI), respectively. The mean achieved peak-signal-to-noise ratios (PSNR) were 27.6 dB and 19.2 dB within the body and tumor ROI, respectively. The achieved mean normalized cross correlation (NCC) values were 0.97 and 0.94 within the body and tumor ROI, respectively. A phantom study demonstrated that the proposed method can accurately re-position the phantom after shift. It is also shown that the proposed method using both kV and MV is superior to current method using kV or MV only in image quality. CONCLUSION: These results demonstrate the feasibility and accuracy of our proposed fast volumetric imaging method from an orthogonal kV/MV pair, which provides a potential solution for daily treatment setup and verification of patients receiving radiation therapy for lung cancer.
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Aprendizado Profundo , Neoplasias Pulmonares , Humanos , Estudos de Viabilidade , Tomografia Computadorizada de Feixe Cônico/métodos , Neoplasias Pulmonares/diagnóstico por imagem , Neoplasias Pulmonares/radioterapia , Pulmão , Imagens de FantasmasRESUMO
BACKGROUND: Multimodality positron emission tomography/computed tomography (PET/CT) imaging combines the anatomical information of CT with the functional information of PET. In the diagnosis and treatment of many cancers, such as non-small cell lung cancer (NSCLC), PET/CT imaging allows more accurate delineation of tumor or involved lymph nodes for radiation planning. PURPOSE: In this paper, we propose a hybrid regional network method of automatically segmenting lung tumors from PET/CT images. METHODS: The hybrid regional network architecture synthesizes the functional and anatomical information from the two image modalities, whereas the mask regional convolutional neural network (R-CNN) and scoring fine-tune the regional location and quality of the output segmentation. This model consists of five major subnetworks, that is, a dual feature representation network (DFRN), a regional proposal network (RPN), a specific tumor-wise R-CNN, a mask-Net, and a score head. Given a PET/CT image as inputs, the DFRN extracts feature maps from the PET and CT images. Then, the RPN and R-CNN work together to localize lung tumors and reduce the image size and feature map size by removing irrelevant regions. The mask-Net is used to segment tumor within a volume-of-interest (VOI) with a score head evaluating the segmentation performed by the mask-Net. Finally, the segmented tumor within the VOI was mapped back to the volumetric coordinate system based on the location information derived via the RPN and R-CNN. We trained, validated, and tested the proposed neural network using 100 PET/CT images of patients with NSCLC. A fivefold cross-validation study was performed. The segmentation was evaluated with two indicators: (1) multiple metrics, including the Dice similarity coefficient, Jacard, 95th percentile Hausdorff distance, mean surface distance (MSD), residual mean square distance, and center-of-mass distance; (2) Bland-Altman analysis and volumetric Pearson correlation analysis. RESULTS: In fivefold cross-validation, this method achieved Dice and MSD of 0.84 ± 0.15 and 1.38 ± 2.2 mm, respectively. A new PET/CT can be segmented in 1 s by this model. External validation on The Cancer Imaging Archive dataset (63 PET/CT images) indicates that the proposed model has superior performance compared to other methods. CONCLUSION: The proposed method shows great promise to automatically delineate NSCLC tumors on PET/CT images, thereby allowing for a more streamlined clinical workflow that is faster and reduces physician effort.
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Carcinoma Pulmonar de Células não Pequenas , Neoplasias Pulmonares , Humanos , Tomografia por Emissão de Pósitrons combinada à Tomografia Computadorizada/métodos , Neoplasias Pulmonares/diagnóstico por imagem , Neoplasias Pulmonares/patologia , Carcinoma Pulmonar de Células não Pequenas/diagnóstico por imagem , Redes Neurais de Computação , Imagem Multimodal , Processamento de Imagem Assistida por Computador/métodosRESUMO
Multiple myeloma (MM) is a hematological malignancy that emerges from antibody-producing plasma B cells. Proteasome inhibitors, including the US Food and Drug Administration-approved bortezomib (BTZ) and carfilzomib (CFZ), are frequently used for the treatment of patients with MM. Nevertheless, a significant proportion of patients with MM are refractory or develop resistance to this class of inhibitors, which represents a significant challenge in the clinic. Thus, identifying factors that determine the potency of proteasome inhibitors in MM is of paramount importance to bolster their efficacy in the clinic. Using genome-wide CRISPR-based screening, we identified a subunit of the mitochondrial pyruvate carrier (MPC) complex, MPC1, as a common modulator of BTZ response in 2 distinct human MM cell lines in vitro. We noticed that CRISPR-mediated deletion or pharmacological inhibition of the MPC complex enhanced BTZ/CFZ-induced MM cell death with minimal impact on cell cycle progression. In fact, targeting the MPC complex compromised the bioenergetic capacity of MM cells, which is accompanied by reduced proteasomal activity, thereby exacerbating BTZ-induced cytotoxicity in vitro. Importantly, we observed that the RNA expression levels of several regulators of pyruvate metabolism were altered in advanced stages of MM for which they correlated with poor patient prognosis. Collectively, this study highlights the importance of the MPC complex for the survival of MM cells and their responses to proteasome inhibitors. These findings establish mitochondrial pyruvate metabolism as a potential target for the treatment of MM and an unappreciated strategy to increase the efficacy of proteasome inhibitors in the clinic.
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Antineoplásicos , Mieloma Múltiplo , Estados Unidos , Humanos , Inibidores de Proteassoma/farmacologia , Inibidores de Proteassoma/uso terapêutico , Mieloma Múltiplo/tratamento farmacológico , Mieloma Múltiplo/patologia , Antineoplásicos/uso terapêutico , Transportadores de Ácidos Monocarboxílicos/uso terapêutico , Bortezomib/farmacologia , Bortezomib/uso terapêutico , Piruvatos/uso terapêuticoRESUMO
Introduction: As immunotherapy has improved distant metastasis-free survival (DMFS) in Non-Small Cell Lung Cancer (NSCLC), isolated locoregional recurrences have increased. However, management of locoregional recurrences can be challenging. We report our institutional experience with definitive intent re-irradiation using Intensity Modulated Proton Therapy (IMPT). Method: Retrospective cohort study of recurrent or second primary NSCLC or LS-SCLC treated with IMPT. Kaplan-Meier method and log-rank test were used for time-to-event analyses. Results: 22 patients were treated from 2019 to 2021. After first course of radiation (median 60 Gy, range 45-70 Gy), 45% received adjuvant immunotherapy. IMPT re-irradiation began a median of 28.2 months (8.8-172.9 months) after initial radiotherapy. The median IMPT dose was 60 GyE (44-60 GyE). 36% received concurrent chemotherapy with IMPT and 18% received immunotherapy after IMPT. The median patient's IMPT lung mean dose was 5.3 GyE (0.9-13.9 GyE) and 5 patients had cumulative esophagus max dose >100 GyE with 1-year overall survival (OS) 68%, 1-year local control 80%, 1-year progression free survival 45%, and 1-year DMFS 60%. Higher IMPT (HR 1.4; 95% CI 1.1-1.7, p=0.01) and initial radiotherapy mean lung doses (HR 1.3; 95% CI 1.0-1.6, p=0.04) were associated with worse OS. Two patients developed Grade 3 pneumonitis or dermatitis, one patient developed Grade 2 pneumonitis, and seven patients developed Grade 1 toxicity. There were no Grade 4 or 5 toxicities. Discussion: Definitive IMPT re-irradiation for lung cancer can prolong disease control with limited toxicity, particularly in the immunotherapy era.
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Implementation of a systematic program for galactic cosmic radiation (GCR) countermeasure discovery will require convenient access to ground-based space radiation analogs. The current gold standard approach for GCR simulation is to use a particle accelerator for sequential irradiation with ion beams representing different GCR components. This has limitations, particularly for studies of non-acute responses, strategies that require robotic instrumentation, or implementation of complex in vitro models that are emerging as alternatives to animal experimentation. Here we explore theoretical and practical issues relating to a different approach to provide a high-LET radiation field for space radiation countermeasure discovery, based on use of compact portable sources to generate neutron-induced charged particles. We present modeling studies showing that DD and DT neutron generators, as well as an AmBe radionuclide-based source, generate charged particles with a linear energy transfer (LET) distribution that, within a range of biological interest extending from about 10 to 200 keV/µm, resembles the LET distribution of reference GCR radiation fields experienced in a spacecraft or on the lunar surface. We also demonstrate the feasibility of using DD neutrons to induce 53BP1 DNA double-strand break repair foci in the HBEC3-KT line of human bronchial epithelial cells, which are widely used for studies of lung carcinogenesis. The neutron-induced foci are larger and more persistent than X ray-induced foci, consistent with the induction of complex, difficult-to-repair DNA damage characteristic of exposure to high-LET (>10 keV/µm) radiation. We discuss limitations of the neutron approach, including low fluence in the low LET range (<10 keV/µm) and the absence of certain long-range features of high charge and energy particle tracks. We present a concept for integration of a compact portable source with a multiplex microfluidic in vitro culture system, and we discuss a pathway for further validation of the use of compact portable sources for countermeasure discovery.
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Radiação Cósmica , Animais , Humanos , Transferência Linear de Energia , Radiação Ionizante , Reparo do DNA , Dano ao DNARESUMO
BACKGROUND/PURPOSE: Higher estimated radiation doses to immune cells (EDIC) have correlated with worse overall survival (OS) in patients with locally-advanced non-small cell lung cancer (NSCLC) prior to the PACIFIC trial, which established consolidative durvalumab as standard-of-care. Here, we examine the prognostic impact of EDIC in the durvalumab era. MATERIALS/METHODS: This single-institution, multi-center study included patients with unresectable stage II/III NSCLC treated with chemoradiation followed by durvalumab. Associations between EDIC [analyzed continuously and categorically (≤6 Gy vs > 6 Gy)] and OS, progression-free survival (PFS), and locoregional control (LRC) were evaluated by Kaplan-Meier and Cox proportional methods. RESULTS: 100 patients were included with median follow-up of 23.7 months. The EDIC > 6 Gy group had a significantly greater percentage of stage IIIB/IIIC disease (76.0 % vs 32.6 %; p < 0.001) and larger tumor volumes (170 cc vs 42 cc; p < 0.001). There were no differences in early durvalumab discontinuation from toxicity (24.1 % vs 15.2 %; p = 0.27). Median OS was shorter among the EDIC > 6 Gy group (29.6 months vs not reached; p < 0.001). On multivariate analysis, EDIC > 6 Gy correlated with worse OS (HR: 4.15, 95 %CI: 1.52-11.33; p = 0.006), PFS (HR: 3.79; 95 %CI: 1.80-8.0; p < 0.001), and LRC (HR: 2.66, 95 %CI: 1.15-6.18; p = 0.023). Analyzed as a continuous variable, higher EDIC was associated with worse OS (HR: 1.34; 95 %CI: 1.16-1.57; p < 0.001), PFS (HR: 1.52; 95 %CI: 1.29-1.79; p < 0.001), and LRC (HR: 1.34, 95 %CI: 1.13-1.60; p = 0.007). CONCLUSIONS: In the immunotherapy era, EDIC is an independent predictor of OS and disease control in locally advanced NSCLC, warranting investigation into techniques to reduce dose to the immune compartment.
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Carcinoma Pulmonar de Células não Pequenas , Neoplasias Pulmonares , Anticorpos Monoclonais/uso terapêutico , Quimiorradioterapia/efeitos adversos , Quimiorradioterapia/métodos , Humanos , Doses de RadiaçãoRESUMO
PURPOSE: Manual delineation on all breathing phases of lung cancer 4D CT image datasets can be challenging, exhaustive, and prone to subjective errors because of both the large number of images in the datasets and variations in the spatial location of tumors secondary to respiratory motion. The purpose of this work is to present a new deep learning-based framework for fast and accurate segmentation of lung tumors on 4D CT image sets. METHODS: The proposed DL framework leverages motion region convolutional neural network (R-CNN). Through integration of global and local motion estimation network architectures, the network can learn both major and minor changes caused by tumor motion. Our network design first extracts tumor motion information by feeding 4D CT images with consecutive phases into an integrated backbone network architecture, locating volume-of-interest (VOIs) via a regional proposal network and removing irrelevant information via a regional convolutional neural network. Extracted motion information is then advanced into the subsequent global and local motion head network architecture to predict corresponding deformation vector fields (DVFs) and further adjust tumor VOIs. Binary masks of tumors are then segmented within adjusted VOIs via a mask head. A self-attention strategy is incorporated in the mask head network to remove any noisy features that might impact segmentation performance. We performed two sets of experiments. In the first experiment, a five-fold cross-validation on 20 4D CT datasets, each consisting of 10 breathing phases (i.e., 200 3D image volumes in total). The network performance was also evaluated on an additional unseen 200 3D images volumes from 20 hold-out 4D CT datasets. In the second experiment, we trained another model with 40 patients' 4D CT datasets from experiment 1 and evaluated on additional unseen nine patients' 4D CT datasets. The Dice similarity coefficient (DSC), center of mass distance (CMD), 95th percentile Hausdorff distance (HD95 ), mean surface distance (MSD), and volume difference (VD) between the manual and segmented tumor contour were computed to evaluate tumor detection and segmentation accuracy. The performance of our method was quantitatively evaluated against four different methods (VoxelMorph, U-Net, network without global and local networks, and network without attention gate strategy) across all evaluation metrics through a paired t-test. RESULTS: The proposed fully automated DL method yielded good overall agreement with the ground truth for contoured tumor volume and segmentation accuracy. Our model yielded significantly better values of evaluation metrics (p < 0.05) than all four competing methods in both experiments. On hold-out datasets of experiment 1 and 2, our method yielded DSC of 0.86 and 0.90 compared to 0.82 and 0.87, 0.75 and 0.83, 081 and 0.89, and 0.81 and 0.89 yielded by VoxelMorph, U-Net, network without global and local networks, and networks without attention gate strategy. Tumor VD between ground truth and our method was the smallest with the value of 0.50 compared to 0.99, 1.01, 0.92, and 0.93 for between ground truth and VoxelMorph, U-Net, network without global and local networks, and networks without attention gate strategy, respectively. CONCLUSIONS: Our proposed DL framework of tumor segmentation on lung cancer 4D CT datasets demonstrates a significant promise for fully automated delineation. The promising results of this work provide impetus for its integration into the 4D CT treatment planning workflow to improve the accuracy and efficiency of lung radiotherapy.
Assuntos
Tomografia Computadorizada Quadridimensional , Neoplasias Pulmonares , Humanos , Processamento de Imagem Assistida por Computador , Neoplasias Pulmonares/diagnóstico por imagem , Movimento (Física) , Redes Neurais de Computação , Carga TumoralRESUMO
Alpha-ketoglutarate (α-KG) is a key metabolite and signaling molecule in cancer cells, but the low permeability of α-KG limits the study of α-KG mediated effects in vivo. Recently, cell-permeable monoester and diester α-KG derivatives have been synthesized for use in vivo, but many of these derivatives are not compatible for use in hyperpolarized carbon-13 nuclear magnetic resonance spectroscopy (HP-13C-MRS). HP-13C-MRS is a powerful technique that has been used to noninvasively trace labeled metabolites in real time. Here, we show that using diethyl-[1-13C]-α-KG as a probe in HP-13C-MRS allows for noninvasive tracing of α-KG metabolism in vivo.