Local heterogeneity of normal lung parenchyma and small airways disease are associated with COPD severity and progression.

BACKGROUND: Small airways disease (SAD) is a major cause of airflow obstruction in COPD patients and has been identified as a precursor to emphysema. Although the amount of SAD in the lungs can be quantified using our Parametric Response Mapping (PRM) approach, the full breadth of this readout as a measure of emphysema and COPD progression has yet to be explored. We evaluated topological features of PRM-derived normal parenchyma and SAD as surrogates of emphysema and predictors of spirometric decline. METHODS: PRM metrics of normal lung (PRMNorm) and functional SAD (PRMfSAD) were generated from CT scans collected as part of the COPDGene study (n = 8956). Volume density (V) and Euler-Poincaré Characteristic (χ) image maps, measures of the extent and coalescence of pocket formations (i.e., topologies), respectively, were determined for both PRMNorm and PRMfSAD. Association with COPD severity, emphysema, and spirometric measures were assessed via multivariable regression models. Readouts were evaluated as inputs for predicting FEV1 decline using a machine learning model. RESULTS: Multivariable cross-sectional analysis of COPD subjects showed that V and χ measures for PRMfSAD and PRMNorm were independently associated with the amount of emphysema. Readouts χfSAD (ß of 0.106, p < 0.001) and VfSAD (ß of 0.065, p = 0.004) were also independently associated with FEV1% predicted. The machine learning model using PRM topologies as inputs predicted FEV1 decline over five years with an AUC of 0.69. CONCLUSIONS: We demonstrated that V and χ of fSAD and Norm have independent value when associated with lung function and emphysema. In addition, we demonstrated that these readouts are predictive of spirometric decline when used as inputs in a ML model. Our topological PRM approach using PRMfSAD and PRMNorm may show promise as an early indicator of emphysema onset and COPD progression.

WITHDRAWN: Oncogenic KRAS G12D extrinsically induces an immunosuppressive microenvironment in lung adenocarcinoma.

This manuscript has been withdrawn by the authors due to a dispute over co-first authorship that is currently being arbitrated by the medical school at our institution. Therefore, the authors do not wish this work to be cited as reference for the project. Upon completion of the arbitration process, we will take steps to revert the current withdrawn status. If you have any questions, please contact the corresponding author.

A road map for the treatment of pediatric diffuse midline glioma.

Recent clinical trials for H3K27-altered diffuse midline gliomas (DMGs) have shown much promise. We present a consensus roadmap and identify three major barriers: (1) refinement of experimental models to include immune and brain-specific components; (2) collaboration among researchers, clinicians, and industry to integrate patient-derived data through sharing, transparency, and regulatory considerations; and (3) streamlining clinical efforts including biopsy, CNS-drug delivery, endpoint determination, and response monitoring. We highlight the importance of comprehensive collaboration to advance the understanding, diagnostics, and therapeutics for DMGs.

Computed tomography-based machine learning for donor lung screening before transplantation.

BACKGROUND: Assessment and selection of donor lungs remain largely subjective and experience based. Criteria to accept or decline lungs are poorly standardized and are not compliant with the current donor pool. Using ex vivo computed tomography (CT) images, we investigated the use of a CT-based machine learning algorithm for screening donor lungs before transplantation. METHODS: Clinical measures and ex situ CT scans were collected from 100 cases as part of a prospective clinical trial. Following procurement, donor lungs were inflated, placed on ice according to routine clinical practice, and imaged using a clinical CT scanner before transplantation while stored in the icebox. We trained and tested a supervised machine learning method called dictionary learning, which uses CT scans and learns specific image patterns and features pertaining to each class for a classification task. The results were evaluated with donor and recipient clinical measures. RESULTS: Of the 100 lung pairs donated, 70 were considered acceptable for transplantation (based on standard clinical assessment) before CT screening and were consequently implanted. The remaining 30 pairs were screened but not transplanted. Our machine learning algorithm was able to detect pulmonary abnormalities on the CT scans. Among the patients who received donor lungs, our algorithm identified recipients who had extended stays in the intensive care unit and were at 19 times higher risk of developing chronic lung allograft dysfunction within 2 years posttransplant. CONCLUSIONS: We have created a strategy to ex vivo screen donor lungs using a CT-based machine learning algorithm. As the use of suboptimal donor lungs rises, it is important to have in place objective techniques that will assist physicians in accurately screening donor lungs to identify recipients most at risk of posttransplant complications.

Modeling Molecular Pathogenesis of Idiopathic Pulmonary Fibrosis-Associated Lung Cancer in Mice.

Idiopathic pulmonary fibrosis (IPF) is characterized by progressive, often fatal loss of lung function due to overactive collagen production and tissue scarring. Patients with IPF have a sevenfold-increased risk of developing lung cancer. The COVID-19 pandemic has increased the number of patients with lung diseases, and infection can worsen prognoses for those with chronic lung diseases and disease-associated cancer. Understanding the molecular pathogenesis of IPF-associated lung cancer is imperative for identifying diagnostic biomarkers and targeted therapies that will facilitate prevention of IPF and progression to lung cancer. To understand how IPF-associated fibroblast activation, matrix remodeling, epithelial-to-mesenchymal transition (EMT), and immune modulation influences lung cancer predisposition, we developed a mouse model to recapitulate the molecular pathogenesis of pulmonary fibrosis-associated lung cancer using the bleomycin and Lewis lung carcinoma models. We demonstrate that development of pulmonary fibrosis-associated lung cancer is likely linked to increased abundance of tumor-associated macrophages and a unique gene signature that supports an immune-suppressive microenvironment through secreted factors. Not surprisingly, preexisting fibrosis provides a pre-metastatic niche and results in augmented tumor growth, and tumors associated with bleomycin-induced fibrosis are characterized by a dramatic loss of cytokeratin expression, indicative of EMT. IMPLICATIONS: This characterization of tumors associated with lung diseases provides new therapeutic targets that may aid in the development of treatment paradigms for lung cancer patients with preexisting pulmonary diseases.

Targeting DNA Repair and Survival Signaling in Diffuse Intrinsic Pontine Gliomas to Prevent Tumor Recurrence.

Therapeutic resistance remains a major obstacle to successful clinical management of diffuse intrinsic pontine glioma (DIPG), a high-grade pediatric tumor of the brain stem. In nearly all patients, available therapies fail to prevent progression. Innovative combinatorial therapies that penetrate the blood-brain barrier and lead to long-term control of tumor growth are desperately needed. We identified mechanisms of resistance to radiotherapy, the standard of care for DIPG. On the basis of these findings, we rationally designed a brain-penetrant small molecule, MTX-241F, that is a highly selective inhibitor of EGFR and PI3 kinase family members, including the DNA repair protein DNA-PK. Preliminary studies demonstrated that micromolar levels of this inhibitor can be achieved in murine brain tissue and that MTX-241F exhibits promising single-agent efficacy and radiosensitizing activity in patient-derived DIPG neurospheres. Its physiochemical properties include high exposure in the brain, indicating excellent brain penetrance. Because radiotherapy results in double-strand breaks that are repaired by homologous recombination (HR) and non-homologous DNA end joining (NHEJ), we have tested the combination of MTX-241F with an inhibitor of Ataxia Telangiectasia Mutated to achieve blockade of HR and NHEJ, respectively, with or without radiotherapy. When HR blockers were combined with MTX-241F and radiotherapy, synthetic lethality was observed, providing impetus to explore this combination in clinically relevant models of DIPG. Our data provide proof-of-concept evidence to support advanced development of MTX-241F for the treatment of DIPG. Future studies will be designed to inform rapid clinical translation to ultimately impact patients diagnosed with this devastating disease.

Topologic Parametric Response Mapping Identifies Tissue Subtypes Associated with Emphysema Progression.

RATIONALE AND OBJECTIVES: Small airways disease (SAD) and emphysema are significant components of chronic obstructive pulmonary disease (COPD), a heterogenous disease where predicting progression is difficult. SAD, a principal cause of airflow obstruction in mild COPD, has been identified as a precursor to emphysema. Parametric Response Mapping (PRM) of chest computed tomography (CT) can help distinguish SAD from emphysema. Specifically, topologic PRM can define local patterns of both diseases to characterize how and in whom COPD progresses. We aimed to determine if distribution of CT-based PRM of functional SAD (fSAD) is associated with emphysema progression. MATERIALS AND METHODS: We analyzed paired inspiratory-expiratory chest CT scans at baseline and 5-year follow up in 1495 COPDGene subjects using topological analyses of PRM classifications. By spatially aligning temporal scans, we mapped local emphysema at year five to baseline lobar PRM-derived topological readouts. K-means clustering was applied to all observations. Subjects were subtyped based on predominant PRM cluster assignments and assessed using non-parametric statistical tests to determine differences in PRM values, pulmonary function metrics, and clinical measures. RESULTS: We identified distinct lobar imaging patterns and classified subjects into three radiologic subtypes: emphysema-dominant (ED), fSAD-dominant (FD), and fSAD-transition (FT: transition from healthy lung to fSAD). Relative to year five emphysema, FT showed rapid local emphysema progression (-57.5% ± 1.1) compared to FD (-49.9% ± 0.5) and ED (-33.1% ± 0.4). FT consisted primarily of at-risk subjects (roughly 60%) with normal spirometry. CONCLUSION: The FT subtype of COPD may allow earlier identification of individuals without spirometrically-defined COPD at-risk for developing emphysema.

Histone H3 K27M-mediated regulation of cancer cell stemness and differentiation in diffuse midline glioma.

Therapeutic resistance remains a major obstacle to preventing progression of H3K27M-altered Diffuse Midline Glioma (DMG). Resistance is driven in part by ALDH-positive cancer stem cells (CSC), with high ALDH1A3 expression observed in H3K27M-mutant DMG biopsies. We hypothesized that ALDH-mediated stemness and resistance may in part be driven by the oncohistone itself. Upon deletion of H3K27M, ALDH1A3 expression decreased dramatically and was accompanied by a gain in astrocytic marker expression and a loss of neurosphere forming potential, indicative of differentiation. Here we show that the oncohistone regulates histone acetylation through ALDH1A3 in a Wnt-dependent manner and that loss of H3K27M expression results in sensitization of DMGs to radiotherapy. The observed elevated Wnt signaling in H3K27M-altered DMG likely stems from a dramatic suppression of mRNA and protein expression of the Wnt inhibitor EYA4 driven by the oncohistone. Thus, our findings identify EYA4 as a bona fide tumor suppressor in DMG that upon suppression, results in aberrant Wnt signaling to orchestrate stemness and differentiation. Future studies will explore whether overexpression of EYA4 in DMG can impede growth and invasion. In summary, we have gained mechanistic insight into H3K27M-mediated regulation of cancer stemness and differentiation, which provides rationale for exploring new therapeutic targets for DMG.

Quantitative CT of Normal Lung Parenchyma and Small Airways Disease Topologies are Associated With COPD Severity and Progression.

Objectives: Small airways disease (SAD) is a major cause of airflow obstruction in COPD patients, and has been identified as a precursor to emphysema. Although the amount of SAD in the lungs can be quantified using our Parametric Response Mapping (PRM) approach, the full breadth of this readout as a measure of emphysema and COPD progression has yet to be explored. We evaluated topological features of PRM-derived normal parenchyma and SAD as surrogates of emphysema and predictors of spirometric decline. Materials and Methods: PRM metrics of normal lung (PRMNorm) and functional SAD (PRMfSAD) were generated from CT scans collected as part of the COPDGene study (n=8956). Volume density (V) and Euler-Poincaré Characteristic (χ) image maps, measures of the extent and coalescence of pocket formations (i.e., topologies), respectively, were determined for both PRMNorm and PRMfSAD. Association with COPD severity, emphysema, and spirometric measures were assessed via multivariable regression models. Readouts were evaluated as inputs for predicting FEV1 decline using a machine learning model. Results: Multivariable cross-sectional analysis of COPD subjects showed that V and χ measures for PRMfSAD and PRMNorm were independently associated with the amount of emphysema. Readouts χfSAD (ß of 0.106, p<0.001) and VfSAD (ß of 0.065, p=0.004) were also independently associated with FEV1% predicted. The machine learning model using PRM topologies as inputs predicted FEV1 decline over five years with an AUC of 0.69. Conclusions: We demonstrated that V and χ of fSAD and Norm have independent value when associated with lung function and emphysema. In addition, we demonstrated that these readouts are predictive of spirometric decline when used as inputs in a ML model. Our topological PRM approach using PRMfSAD and PRMNorm may show promise as an early indicator of emphysema onset and COPD progression.

Lung cancer lesion detection in histopathology images using graph-based sparse PCA network.

Early detection of lung cancer is critical for improvement of patient survival. To address the clinical need for efficacious treatments, genetically engineered mouse models (GEMM) have become integral in identifying and evaluating the molecular underpinnings of this complex disease that may be exploited as therapeutic targets. Assessment of GEMM tumor burden on histopathological sections performed by manual inspection is both time consuming and prone to subjective bias. Therefore, an interplay of needs and challenges exists for computer-aided diagnostic tools, for accurate and efficient analysis of these histopathology images. In this paper, we propose a simple machine learning approach called the graph-based sparse principal component analysis (GS-PCA) network, for automated detection of cancerous lesions on histological lung slides stained by hematoxylin and eosin (H&E). Our method comprises four steps: 1) cascaded graph-based sparse PCA, 2) PCA binary hashing, 3) block-wise histograms, and 4) support vector machine (SVM) classification. In our proposed architecture, graph-based sparse PCA is employed to learn the filter banks of the multiple stages of a convolutional network. This is followed by PCA hashing and block histograms for indexing and pooling. The meaningful features extracted from this GS-PCA are then fed to an SVM classifier. We evaluate the performance of the proposed algorithm on H&E slides obtained from an inducible K-rasG12D lung cancer mouse model using precision/recall rates, Fß-score, Tanimoto coefficient, and area under the curve (AUC) of the receiver operator characteristic (ROC) and show that our algorithm is efficient and provides improved detection accuracy compared to existing algorithms.

CT-based Machine Learning for Donor Lung Screening Prior to Transplantation.

Background: Assessment and selection of donor lungs remains largely subjective and experience based. Criteria to accept or decline lungs are poorly standardized and are not compliant with the current donor pool. Using ex vivo CT images, we investigated the use of a CT-based machine learning algorithm for screening donor lungs prior to transplantation. Methods: Clinical measures and ex-situ CT scans were collected from 100 cases as part of a prospective clinical trial. Following procurement, donor lungs were inflated, placed on ice according to routine clinical practice, and imaged using a clinical CT scanner prior to transplantation while stored in the icebox. We trained and tested a supervised machine learning method called dictionary learning , which uses CT scans and learns specific image patterns and features pertaining to each class for a classification task. The results were evaluated with donor and recipient clinical measures. Results: Of the 100 lung pairs donated, 70 were considered acceptable for transplantation (based on standard clinical assessment) prior to CT screening and were consequently implanted. The remaining 30 pairs were screened but not transplanted. Our machine learning algorithm was able to detect pulmonary abnormalities on the CT scans. Among the patients who received donor lungs, our algorithm identified recipients who had extended stays in the ICU and were at 19 times higher risk of developing CLAD within 2 years post-transplant. Conclusions: We have created a strategy to ex vivo screen donor lungs using a CT-based machine learning algorithm. As the use of suboptimal donor lungs rises, it is important to have in place objective techniques that will assist physicians in accurately screening donor lungs to identify recipients most at risk of post-transplant complications.

Therapeutic avenues for targeting treatment challenges of diffuse midline gliomas.

Diffuse midline glioma (DMG) is the leading cause of brain tumor-related deaths in children. DMG typically presents with variable neurologic symptoms between ages 3 and 10. Currently, radiation remains the standard therapy for DMG to halt progression and reduce tumor bulk to minimize symptoms. However, tumors recur in almost 100% of patients and thus, DMG is still considered an incurable cancer with a median survival of 9-12 months. Surgery is generally contraindicated due to the delicate organization of the brainstem, where DMG is located. Despite extensive research efforts, no chemotherapeutic agents, immune therapies, or molecularly targeted therapies have been approved to provide survival benefit. Furthermore, the efficacy of therapies is limited by poor blood-brain barrier penetration and inherent resistance mechanisms of the tumor. However, novel drug delivery approaches, along with recent advances in molecularly targeted therapies and immunotherapies, have advanced to clinical trials and may provide viable future treatment options for DMG patients. This review seeks to evaluate current therapeutics at the preclinical stage and those that have advanced to clinical trials and to discuss the challenges of drug delivery and inherent resistance to these therapies.

Metabolic requirement for GOT2 in pancreatic cancer depends on environmental context.

Mitochondrial glutamate-oxaloacetate transaminase 2 (GOT2) is part of the malate-aspartate shuttle, a mechanism by which cells transfer reducing equivalents from the cytosol to the mitochondria. GOT2 is a key component of mutant KRAS (KRAS*)-mediated rewiring of glutamine metabolism in pancreatic ductal adenocarcinoma (PDA). Here, we demonstrate that the loss of GOT2 disturbs redox homeostasis and halts proliferation of PDA cells in vitro. GOT2 knockdown (KD) in PDA cell lines in vitro induced NADH accumulation, decreased Asp and α-ketoglutarate (αKG) production, stalled glycolysis, disrupted the TCA cycle, and impaired proliferation. Oxidizing NADH through chemical or genetic means resolved the redox imbalance induced by GOT2 KD, permitting sustained proliferation. Despite a strong in vitro inhibitory phenotype, loss of GOT2 had no effect on tumor growth in xenograft PDA or autochthonous mouse models. We show that cancer-associated fibroblasts (CAFs), a major component of the pancreatic tumor microenvironment (TME), release the redox active metabolite pyruvate, and culturing GOT2 KD cells in CAF conditioned media (CM) rescued proliferation in vitro. Furthermore, blocking pyruvate import or pyruvate-to-lactate reduction prevented rescue of GOT2 KD in vitro by exogenous pyruvate or CAF CM. However, these interventions failed to sensitize xenografts to GOT2 KD in vivo, demonstrating the remarkable plasticity and differential metabolism deployed by PDA cells in vitro and in vivo. This emphasizes how the environmental context of distinct pre-clinical models impacts both cell-intrinsic metabolic rewiring and metabolic crosstalk with the TME.

Investigating the Incidence of Pulmonary Abnormalities as Identified by Parametric Response Mapping in Patients With Lung Cancer Before Radiation Treatment.

Purpose: Parametric response mapping (PRM) of high-resolution, paired inspiration and expiration computed tomography (CT) scans is a promising analytical imaging technique that is currently used in diagnostic applications and offers the ability to characterize and quantify certain pulmonary pathologies on a patient-specific basis. As one of the first studies to implement such a technique in the radiation oncology clinic, the goal of this work was to assess the feasibility for PRM analysis to identify pulmonary abnormalities in patients with lung cancer before radiation therapy (RT). Methods and Materials: High-resolution, paired inspiration and expiration CT scans were acquired from 23 patients with lung cancer as part of routine treatment planning CT acquisition. When applied to the paired CT scans, PRM analysis classifies lung parenchyma, on a voxel-wise basis, as normal, small airways disease (SAD), emphysema, or parenchymal disease (PD). PRM classifications were quantified as a percent of total lung volume and were evaluated globally and regionally within the lung. Results: PRM analysis of pre-RT CT scans was successfully implemented using a workflow that produced patient-specific maps and quantified specific phenotypes of pulmonary abnormalities. Through this study, a large prevalence of SAD and PD was demonstrated in this lung cancer patient population, with global averages of 10% and 17%, respectively. Moreover, PRM-classified normal and SAD in the region with primary tumor involvement were found to be significantly different from global lung values. When present, elevated levels of PD and SAD abnormalities tended to be pervasive in multiple regions of the lung, indicating a large burden of underlying disease. Conclusions: Pulmonary abnormalities, as detected by PRM, were characterized in patients with lung cancer scheduled for RT. Although further study is needed, PRM is a highly accessible CT-based imaging technique that has the potential to identify local lung abnormalities associated with chronic obstructive pulmonary disease and interstitial lung disease. Further investigation in the radiation oncology setting may provide strategies for tailoring RT planning and risk assessment based on pre-existing PRM-based pathology.

Quantitative CT Correlates with Local Inflammation in Lung of Patients with Subtypes of Chronic Lung Allograft Dysfunction.

Chronic rejection of lung allografts has two major subtypes, bronchiolitis obliterans syndrome (BOS) and restrictive allograft syndrome (RAS), which present radiologically either as air trapping with small airways disease or with persistent pleuroparenchymal opacities. Parametric response mapping (PRM), a computed tomography (CT) methodology, has been demonstrated as an objective readout of BOS and RAS and bears prognostic importance, but has yet to be correlated to biological measures. Using a topological technique, we evaluate the distribution and arrangement of PRM-derived classifications of pulmonary abnormalities from lung transplant recipients undergoing redo-transplantation for end-stage BOS (N = 6) or RAS (N = 6). Topological metrics were determined from each PRM classification and compared to structural and biological markers determined from microCT and histopathology of lung core samples. Whole-lung measurements of PRM-defined functional small airways disease (fSAD), which serves as a readout of BOS, were significantly elevated in BOS versus RAS patients (p = 0.01). At the core-level, PRM-defined parenchymal disease, a potential readout of RAS, was found to correlate to neutrophil and collagen I levels (p < 0.05). We demonstrate the relationship of structural and biological markers to the CT-based distribution and arrangement of PRM-derived readouts of BOS and RAS.

Improved detection of air trapping on expiratory computed tomography using deep learning.

BACKGROUND: Radiologic evidence of air trapping (AT) on expiratory computed tomography (CT) scans is associated with early pulmonary dysfunction in patients with cystic fibrosis (CF). However, standard techniques for quantitative assessment of AT are highly variable, resulting in limited efficacy for monitoring disease progression. OBJECTIVE: To investigate the effectiveness of a convolutional neural network (CNN) model for quantifying and monitoring AT, and to compare it with other quantitative AT measures obtained from threshold-based techniques. MATERIALS AND METHODS: Paired volumetric whole lung inspiratory and expiratory CT scans were obtained at four time points (0, 3, 12 and 24 months) on 36 subjects with mild CF lung disease. A densely connected CNN (DN) was trained using AT segmentation maps generated from a personalized threshold-based method (PTM). Quantitative AT (QAT) values, presented as the relative volume of AT over the lungs, from the DN approach were compared to QAT values from the PTM method. Radiographic assessment, spirometric measures, and clinical scores were correlated to the DN QAT values using a linear mixed effects model. RESULTS: QAT values from the DN were found to increase from 8.65% ± 1.38% to 21.38% ± 1.82%, respectively, over a two-year period. Comparison of CNN model results to intensity-based measures demonstrated a systematic drop in the Dice coefficient over time (decreased from 0.86 ± 0.03 to 0.45 ± 0.04). The trends observed in DN QAT values were consistent with clinical scores for AT, bronchiectasis, and mucus plugging. In addition, the DN approach was found to be less susceptible to variations in expiratory deflation levels than the threshold-based approach. CONCLUSION: The CNN model effectively delineated AT on expiratory CT scans, which provides an automated and objective approach for assessing and monitoring AT in CF patients.

Transcriptomic Analysis of Diffuse Intrinsic Pontine Glioma (DIPG) Identifies a Targetable ALDH-Positive Subset of Highly Tumorigenic Cancer Stem-like Cells.

Understanding the cancer stem cell (CSC) landscape in diffuse intrinsic pontine glioma (DIPG) is desperately needed to address treatment resistance and identify novel therapeutic approaches. Patient-derived DIPG cells demonstrated heterogeneous expression of aldehyde dehydrogenase (ALDH) and CD133 by flow cytometry. Transcriptome-level characterization identified elevated mRNA levels of MYC, E2F, DNA damage repair (DDR) genes, glycolytic metabolism, and mTOR signaling in ALDH+ compared with ALDH-, supporting a stem-like phenotype and indicating a druggable target. ALDH+ cells demonstrated increased proliferation, neurosphere formation, and initiated tumors that resulted in decreased survival when orthotopically implanted. Pharmacologic MAPK/PI3K/mTOR targeting downregulated MYC, E2F, and DDR mRNAs and reduced glycolytic metabolism. In vivo PI3K/mTOR targeting inhibited tumor growth in both flank and an ALDH+ orthotopic tumor model likely by reducing cancer stemness. In summary, we describe existence of ALDH+ DIPGs with proliferative properties due to increased metabolism, which may be regulated by the microenvironment and likely contributing to drug resistance and tumor recurrence. IMPLICATIONS: Characterization of ALDH+ DIPGs coupled with targeting MAPK/PI3K/mTOR signaling provides an impetus for molecularly targeted therapy aimed at addressing the CSC phenotype in DIPG.

Stereotactic Transcranial Focused Ultrasound Targeting System for Murine Brain Models.

An inexpensive, accurate focused ultrasound stereotactic targeting method guided by pretreatment magnetic resonance imaging (MRI) images for murine brain models is presented. An uncertainty of each sub-component of the stereotactic system was analyzed. The entire system was calibrated using clot phantoms. The targeting accuracy of the system was demonstrated with an in vivo mouse glioblastoma (GBM) model. The accuracy was quantified by the absolute distance difference between the prescribed and ablated points visible on the pre treatment and posttreatment MR images, respectively. A precalibration phantom study ( N = 6 ) resulted in an error of 0.32 ± 0.31, 0.72 ± 0.16, and 1.06 ± 0.38 mm in axial, lateral, and elevational axes, respectively. A postcalibration phantom study ( N = 8 ) demonstrated a residual error of 0.09 ± 0.01, 0.15 ± 0.09, and 0.47 ± 0.18 mm in axial, lateral, and elevational axes, respectively. The calibrated system showed significantly reduced ( ) error of 0.20 ± 0.21, 0.34 ± 0.24, and 0.28 ± 0.21 mm in axial, lateral, and elevational axes, respectively, in the in vivo GBM tumor-bearing mice ( N = 10 ).

The cytochrome P450 enzyme CYP24A1 increases proliferation of mutant KRAS-dependent lung adenocarcinoma independent of its catalytic activity.

We previously reported that overexpression of cytochrome P450 family 24 subfamily A member 1 (CYP24A1) increases lung cancer cell proliferation by activating RAS signaling and that CYP24A1 knockdown inhibits tumor growth. However, the mechanism of CYP24A1-mediated cancer cell proliferation remains unclear. Here, we conducted cell synchronization and biochemical experiments in lung adenocarcinoma cells, revealing a link between CYP24A1 and anaphase-promoting complex (APC), a key cell cycle regulator. We demonstrate that CYP24A1 expression is cell cycle-dependent; it was higher in the G2-M phase and diminished upon G1 entry. CYP24A1 has a functional destruction box (D-box) motif that allows binding with two APC adaptors, CDC20-homologue 1 (CDH1) and cell division cycle 20 (CDC20). Unlike other APC substrates, however, CYP24A1 acted as a pseudo-substrate, inhibiting CDH1 activity and promoting mitotic progression. Conversely, overexpression of a CYP24A1 D-box mutant compromised CDH1 binding, allowing CDH1 hyperactivation, thereby hastening degradation of its substrates cyclin B1 and CDC20, and accumulation of the CDC20 substrate p21, prolonging mitotic exit. These activities also occurred with a CYP24A1 isoform 2 lacking the catalytic cysteine (Cys-462), suggesting that CYP24A1's oncogenic potential is independent of its catalytic activity. CYP24A1 degradation reduced clonogenic survival of mutant KRAS-driven lung cancer cells, and calcitriol treatment increased CYP24A1 levels and tumor burden in Lsl-KRASG12D mice. These results disclose a catalytic activity-independent growth-promoting role of CYP24A1 in mutant KRAS-driven lung cancer. This suggests that CYP24A1 could be therapeutically targeted in lung cancers in which its expression is high.

Multicenter evaluation of parametric response mapping as an indicator of bronchiolitis obliterans syndrome after hematopoietic stem cell transplantation.

Parametric response mapping (PRM) is a novel computed tomography (CT) technology that has shown potential for assessment of bronchiolitis obliterans syndrome (BOS) after hematopoietic stem cell transplantation (HCT). The primary aim of this study was to evaluate whether variations in image acquisition under real-world conditions affect the PRM measurements of clinically diagnosed BOS. CT scans were obtained retrospectively from 72 HCT recipients with BOS and graft-versus-host disease from Fred Hutchinson Cancer Research Center, Karolinska Institute, and the University of Michigan. Whole lung volumetric scans were performed at inspiration and expiration using site-specific acquisition and reconstruction protocols. PRM and pulmonary function measurements were assessed. Patients with moderately severe BOS at diagnosis (median forced expiratory volume at 1 second [FEV1] 53.5% predicted) had similar characteristics between sites. Variations in site-specific CT acquisition protocols had a negligible effect on the PRM-derived small airways disease (SAD), that is, BOS measurements. PRM-derived SAD was found to correlate with FEV1% predicted and FEV1/ forced vital capacity (R = -0.236, P = .046; and R = -0.689, P < .0001, respectively), which suggests that elevated levels in the PRM measurements are primarily affected by BOS airflow obstruction and not CT scan acquisition parameters. Based on these results, PRM may be applied broadly for post-HCT diagnosis and monitoring of BOS.