Identification of a fundamental cryoinjury mechanism in MSCs and its mitigation through cell-cycle synchronization prior to freezing.

Clinical development of cellular therapies, including mesenchymal stem/stromal cell (MSC) treatments, has been hindered by ineffective cryopreservation methods that result in substantial loss of post-thaw cell viability and function. Proposed solutions to generate high potency MSC for clinical testing include priming cells with potent cytokines such as interferon gamma (IFNγ) prior to cryopreservation, which has been shown to enhance post-thaw function, or briefly culturing to allow recovery from cryopreservation injury prior to administering to patients. However, both solutions have disadvantages: cryorecovery increases the complexity of manufacturing and distribution logistics, while the pleiotropic effects of IFNγ may have uncharacterized and unintended consequences on MSC function. To determine specific cellular functions impacted by cryoinjury, we first evaluated cell cycle status. It was discovered that S phase MSC are exquisitely sensitive to cryoinjury, demonstrating heightened levels of delayed apoptosis post-thaw and reduced immunomodulatory function. Blocking cell cycle progression at G0/G1 by growth factor deprivation (commonly known as serum starvation) greatly reduced post-thaw dysfunction of MSC by preventing apoptosis induced by double-stranded breaks in labile replicating DNA that form during the cryopreservation and thawing processes. Viability, clonal growth and T cell suppression function were preserved at pre-cryopreservation levels and were no different than cells prior to freezing or frozen after priming with IFNγ. Thus, we have developed a robust and effective strategy to enhance post-thaw recovery of therapeutic MSC.

CT-based radiomics to predict development of macrovascular invasion in hepatocellular carcinoma: A multicenter study.

BACKGROUND: Macrovascular invasion (MaVI) occurs in nearly half of hepatocellular carcinoma (HCC) patients at diagnosis or during follow-up, which causes severe disease deterioration, and limits the possibility of surgical approaches. This study aimed to investigate whether computed tomography (CT)-based radiomics analysis could help predict development of MaVI in HCC. METHODS: A cohort of 226 patients diagnosed with HCC was enrolled from 5 hospitals with complete MaVI and prognosis follow-ups. CT-based radiomics signature was built via multi-strategy machine learning methods. Afterwards, MaVI-related clinical factors and radiomics signature were integrated to construct the final prediction model (CRIM, clinical-radiomics integrated model) via random forest modeling. Cox-regression analysis was used to select independent risk factors to predict the time of MaVI development. Kaplan-Meier analysis was conducted to stratify patients according to the time of MaVI development, progression-free survival (PFS), and overall survival (OS) based on the selected risk factors. RESULTS: The radiomics signature showed significant improvement for MaVI prediction compared with conventional clinical/radiological predictors (P < 0.001). CRIM could predict MaVI with satisfactory areas under the curve (AUC) of 0.986 and 0.979 in the training (n = 154) and external validation (n = 72) datasets, respectively. CRIM presented with excellent generalization with AUC of 0.956, 1.000, and 1.000 in each external cohort that accepted disparate CT scanning protocol/manufactory. Peel9_fos_InterquartileRange [hazard ratio (HR) = 1.98; P < 0.001] was selected as the independent risk factor. The cox-regression model successfully stratified patients into the high-risk and low-risk groups regarding the time of MaVI development (P < 0.001), PFS (P < 0.001) and OS (P = 0.002). CONCLUSIONS: The CT-based quantitative radiomics analysis could enable high accuracy prediction of subsequent MaVI development in HCC with prognostic implications.

Development and External Validation of Radiomics Approach for Nuclear Grading in Clear Cell Renal Cell Carcinoma.

BACKGROUND AND PURPOSE: Nuclear grades of clear cell renal cell carcinoma (ccRCC) are usually confirmed by invasive methods. Radiomics is a quantitative tool that uses non-invasive medical imaging for tumor diagnosis and prognosis. In this study, a radiomics approach was proposed to analyze the association between preoperative computed tomography (CT) images and nuclear grades of ccRCC. METHODS: Our dataset included 320 ccRCC patients from two centers and was divided into a training set (n = 124), an internal test set (n = 123), and an external test set (n = 73). A radiomic feature set was extracted from unenhanced, corticomedullary phase, and nephrographic phase CT images. The maximizing independent classification information criteria function and recursive feature elimination with cross-validation were used to select effective features. Random forests were used to build a final model for predicting nuclear grades, and area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of radiomic features and models. RESULTS: The radiomic features from the three CT phases could effectively distinguished the four nuclear grades. A combined model, merging radiomic features and clinical characteristics, obtained good predictive performances in the internal test set (AUC 0.77, 0.75, 0.79, and 0.85 for the four grades, respectively), and performance was further confirmed in the external test set, with AUCs of 0.75, 0.68, and 0.73 (no fourth-level data). CONCLUSION: The combination of CT radiomic features and clinical characteristics could discriminate the nuclear grades in ccRCC, which may help in assisting treatment decision making.

Identification and characterization of a large source of primary mesenchymal stem cells tightly adhered to bone surfaces of human vertebral body marrow cavities.

BACKGROUND: Therapeutic allogeneic mesenchymal stromal cells (MSCs) are currently in clinical trials to evaluate their effectiveness in treating many different disease indications. Eventual commercialization for broad distribution will require further improvements in manufacturing processes to economically manufacture MSCs at scales sufficient to satisfy projected demands. A key contributor to the present high cost of goods sold for MSC manufacturing is the need to create master cell banks from multiple donors, which leads to variability in large-scale manufacturing runs. Therefore, the availability of large single donor depots of primary MSCs would greatly benefit the cell therapy market by reducing costs associated with manufacturing. METHODS: We have discovered that an abundant population of cells possessing all the hallmarks of MSCs is tightly associated with the vertebral body (VB) bone matrix and only liberated by proteolytic digestion. Here we demonstrate that these vertebral bone-adherent (vBA) MSCs possess all the International Society of Cell and Gene Therapy-defined characteristics (e.g., plastic adherence, surface marker expression and trilineage differentiation) of MSCs, and we have therefore termed them vBA-MSCs to distinguish this population from loosely associated MSCs recovered through aspiration or rinsing of the bone marrow compartment. RESULTS: Pilot banking and expansion were performed with vBA-MSCs obtained from 3 deceased donors, and it was demonstrated that bank sizes averaging 2.9 × 108 ± 1.35 × 108 vBA-MSCs at passage 1 were obtainable from only 5 g of digested VB bone fragments. Each bank of cells demonstrated robust proliferation through a total of 9 passages, without significant reduction in population doubling times. The theoretical total cell yield from the entire amount of bone fragments (approximately 300 g) from each donor with limited expansion through 4 passages is 100 trillion (1 × 1014) vBA-MSCs, equating to over 105 doses at 10 × 106 cells/kg for an average 70-kg recipient. DISCUSSION: Thus, we have established a novel and plentiful source of MSCs that will benefit the cell therapy market by overcoming manufacturing and regulatory inefficiencies due to donor-to-donor variability.

MRI-Based Radiomics Signature: A Potential Biomarker for Identifying Glypican 3-Positive Hepatocellular Carcinoma.

BACKGROUND: Glypican 3 (GPC3) expression has proved to be a critical risk factor related to prognosis in hepatocellular carcinoma (HCC) patients. PURPOSE: To investigate the performance of MRI-based radiomics signature in identifying GPC3-positive HCC. STUDY TYPE: Retrospective. POPULATION: An initial cohort of 293 patients with pathologically confirmed HCC was involved in this study, and patients were randomly divided into training (195) and validation (98) cohorts. FIELD STRENGTH/SEQUENCES: Contrast-enhanced T1 -weight MRI was performed with a 1.5T scanner. ASSESSMENT: A total of 853 radiomic features were extracted from the volume imaging. Univariate analysis and Fisher scoring were utilized for feature reduction. Subsequently, forward stepwise feature selection and radiomics signature building were performed based on a support vector machine (SVM). Incorporating independent risk factors, a combined nomogram was developed by multivariable logistic regression modeling. STATISTICAL TESTS: The predictive performance of the nomogram was calculated using the area under the receive operating characteristic curve (AUC). Decision curve analysis (DCA) was applied to estimate the clinical usefulness. RESULTS: The radiomics signature consisting of 10 selected features achieved good prediction efficacy (training cohort: AUC = 0.879, validation cohort: AUC = 0.871). Additionally, the combined nomogram integrating independent clinical risk factor α-fetoprotein (AFP) and radiomics signature showed improved calibration and prominent predictive performance with AUCs of 0.926 and 0.914 in the training and validation cohorts, respectively. DATA CONCLUSION: The proposed MR-based radiomics signature is strongly related to GPC3-positive. The combined nomogram incorporating AFP and radiomics signature may provide an effective tool for noninvasive and individualized prediction of GPC3-positive in patients with HCC. J. MAGN. RESON. IMAGING 2020;52:1679-1687.

Radiomics Based on MRI as a Biomarker to Guide Therapy by Predicting Upgrading of Prostate Cancer From Biopsy to Radical Prostatectomy.

BACKGROUND: Biopsy Gleason score (GS) is crucial for prostate cancer (PCa) treatment decision-making. Upgrading in GS from biopsy to radical prostatectomy (RP) puts a proportion of patients at risk of undertreatment. PURPOSE: To develop and validate a radiomics model based on multiparametric magnetic resonance imaging (mp-MRI) to predict PCa upgrading. STUDY TYPE: Retrospective, radiomics. POPULATION: A total of 166 RP-confirmed PCa patients (training cohort, n = 116; validation cohort, n = 50) were included. FIELD STRENGTH/SEQUENCE: 3.0T/T2 -weighted (T2 W), apparent diffusion coefficient (ADC), and dynamic contrast enhancement (DCE) sequences. ASSESSMENT: PI-RADSv2 score for each tumor was recorded. Radiomic features were extracted from T2 W, ADC, and DCE sequences and Mutual Information Maximization criterion was used to identify the optimal features on each sequence. Multivariate logistic regression analysis was used to develop predictive models and a radiomics nomogram and their performance was evaluated. STATISTICAL TESTS: Student's t or chi-square were used to assess the differences in clinicopathologic data between the training and validation cohorts. Receiver operating characteristic (ROC) curve analysis was performed and the area under the curve (AUC) was calculated. RESULTS: In PI-RADSv2 assessment, 67 lesions scored 5, 70 lesions scored 4, and 29 lesions scored 3. For each sequence, 4404 features were extracted and the top 20 best features were selected. The radiomics model incorporating signatures from the three sequences achieved better performance than any single sequence (AUC: radiomics model 0.868, T2 W 0.700, ADC 0.759, DCE 0.726). The combined mode incorporating radiomics signature, clinical stage, and time from biopsy to RP outperformed the clinical model and radiomics model (AUC: combined model 0.910, clinical model 0.646, radiomics model 0.868). The nomogram showed good performance (AUC 0.910) and calibration (P-values: training cohort 0.624, validation cohort 0.294). DATA CONCLUSION: Radiomics based on mp-MRI has potential to predict upgrading of PCa from biopsy to RP. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 5 J. Magn. Reson. Imaging 2020;52:1239-1248.

Radiomics in liver diseases: Current progress and future opportunities.

Liver diseases, a wide spectrum of pathologies from inflammation to neoplasm, have become an increasingly significant health problem worldwide. Noninvasive imaging plays a critical role in the clinical workflow of liver diseases, but conventional imaging assessment may provide limited information. Accurate detection, characterization and monitoring remain challenging. With progress in quantitative imaging analysis techniques, radiomics emerged as an efficient tool that shows promise to aid in personalized diagnosis and treatment decision-making. Radiomics could reflect the heterogeneity of liver lesions via extracting high-throughput and high-dimensional features from multi-modality imaging. Machine learning algorithms are then used to construct clinical target-oriented imaging biomarkers to assist disease management. Here, we review the methodological process in liver disease radiomics studies in a stepwise fashion from data acquisition and curation, region of interest segmentation, liver-specific feature extraction, to task-oriented modelling. Furthermore, the applications of radiomics in liver diseases are outlined in aspects of diagnosis and staging, evaluation of liver tumour biological behaviours, and prognosis according to different disease type. Finally, we discuss the current limitations of radiomics in liver disease studies and explore its future opportunities.

A radiomics-based biomarker for cytokeratin 19 status of hepatocellular carcinoma with gadoxetic acid-enhanced MRI.

OBJECTIVES: We aimed to develop a radiomics-based model derived from gadoxetic acid-enhanced MR images to preoperatively identify cytokeratin (CK) 19 status of hepatocellular carcinoma (HCC). METHODS: A cohort of 227 patients with single HCC was classified into a training set (n = 159) and a time-independent validated set (n = 68). A total of 647 radiomic features were extracted from multi-sequence MR images. The least absolute shrinkage and selection operator regression and decision tree methods were utilized for feature selection and radiomics signature construction. A multivariable logistic regression model incorporating clinico-radiological features and the fusion radiomics signature was built for prediction of CK19 status by evaluating area under curve (AUC). RESULTS: In the whole cohort, 57 patients were CK19 positive and 170 patients were CK19 negative. By combining 11 and 6 radiomic features extracted in arterial phase and hepatobiliary phase images, respectively, a fusion radiomics signature achieved AUCs of 0.951 and 0.822 in training and validation datasets. The final combined model integrated a-fetoprotein levels, arterial rim enhancement pattern, irregular tumor margin, and the fusion radiomics signature, with a sensitivity of 0.818 and specificity of 0.974 in the training cohort and that of 0.769 and 0.818 in the validated cohort. The nomogram based on the combined model showed satisfactory prediction performance in training (C-index 0.959) and validation (C-index 0.846) dataset. CONCLUSIONS: The combined model based on a fusion radiomics signature derived from arterial and hepatobiliary phase images of gadoxetic acid-enhanced MRI can be a reliable biomarker for CK19 status of HCC. KEY POINTS: • Arterial rim enhancement pattern and irregular tumor margin on hepatobiliary phase on gadoxetic acid-enhanced MRI can be useful for evaluating CK19 status of HCC. • A radiomics-based model performed better than the clinico-radiological model both in training and validation datasets for predicting CK19 status of HCC. • The nomogram based on the fusion radiomics signature can be easily used for CK19 stratification of HCC.

Prediction of Histopathologic Growth Patterns of Colorectal Liver Metastases with a Noninvasive Imaging Method.

OBJECTIVES: To predict histopathologic growth patterns (HGPs) in colorectal liver metastases (CRLMs) with a noninvasive radiomics model. METHODS: Patients with chemotherapy-naive CRLMs who underwent abdominal contrast-enhanced multidetector CT (MDCT) followed by partial hepatectomy between January 2007 and January 2019 from two institutions were included in this retrospective study. Hematoxylin- and eosin-stained histopathologic sections of CRLMs were reviewed, with HGPs defined according to international consensus. Lesions were divided into training and validation datasets based on patients' sources. Radiomic features were extracted from pre- and post-contrast (arterial and portal venous) phase MDCT images, with review focusing on the segmented tumor-liver interface zones of CRLMs. Minimum redundancy maximum relevance and decision tree methods were used for radiomics modeling. Multivariable logistic regression analyses and ROC curves were used to assess the predictive performance of these models in predicting HGP types. RESULTS: A total of 126 CRLMs with histopathologic-demonstrated desmoplastic (n = 68) or replacement (n = 58) HGPs were assessed. The radiomics signature consisted of 20 features of each phase selected. The 3 phases fused radiomics signature demonstrated the best predictive performance in distinguishing between replacement and desmoplastic HGPs (AUCs of 0.926 and 0.939 in the training and external validation cohorts, respectively). The clinical-radiomics combined model showed good discrimination (C-indices of 0.941 and 0.833 in the training and external validation cohorts, respectively). CONCLUSIONS: A radiomics model derived from MDCT images may effectively predict the HGP of CRLMs, thus providing a basis for prognostic stratification and therapeutic decision-making.

Preoperative radiomics nomogram for microvascular invasion prediction in hepatocellular carcinoma using contrast-enhanced CT.

OBJECTIVES: To develop and validate a radiomics nomogram for preoperative prediction of microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC). METHODS: The study included 157 patients with histologically confirmed HCC with or without MVI, and 110 patients were allocated to the training dataset and 47 to the validation dataset. Baseline clinical factor (CF) data were collected from our medical records, and radiomics features were extracted from the artery phase (AP), portal venous phase (PVP) and delay phase (DP) of preoperatively acquired CT in all patients. Radiomics analysis included tumour segmentation, feature extraction, model construction and model evaluation. A final nomogram for predicting MVI of HCC was established. Nomogram performance was assessed via both calibration and discrimination statistics. RESULTS: Five AP features, seven PVP features and nine DP features were effective for MVI prediction in HCC radiomics signatures. PVP radiomics signatures exhibited better performance than AP and DP radiomics signatures in the validation datasets, with the AUC 0.793. In the clinical model, age, maximum tumour diameter, alpha-fetoprotein and hepatitis B antigen were effective predictors. The final nomogram integrated the PVP radiomics signature and four CFs. Good calibration was achieved for the nomogram in both the training and validated datasets, with respective C-indexes of 0.827 and 0.820. Decision curve analysis suggested that the proposed nomogram was clinically useful, with a corresponding net benefit of 0.357. CONCLUSIONS: The above-described radiomics nomogram can preoperatively predict MVI in patients with HCC and may constitute a usefully clinical tool to guide subsequent personalised treatment. KEY POINTS: • No previously reported study has utilised radiomics nomograms to preoperatively predict the MVI of HCC using 3D contrast-enhanced CT imaging. • The combined radiomics clinical factor (CF) nomogram for predicting MVI achieved superior performance than either the radiomics signature or the CF nomogram alone. • Nomograms combing PVP radiomics and CF may be useful as an imaging marker for predicting MVI of HCC preoperatively and could guide personalised treatment.

CT radiomics may predict the grade of pancreatic neuroendocrine tumors: a multicenter study.

OBJECTIVE: To develop and validate a radiomics-based nomogram for preoperatively predicting grade 1 and grade 2/3 tumors in patients with pancreatic neuroendocrine tumors (PNETs). METHODS: One hundred thirty-eight patients derived from two institutions with pathologically confirmed PNETs (104 in the training cohort and 34 in the validation cohort) were included in this retrospective study. A total of 853 radiomic features were extracted from arterial and portal venous phase CT images respectively. Minimum redundancy maximum relevance and random forest methods were adopted for the significant radiomic feature selection and radiomic signature construction. A fusion radiomic signature was generated by combining both the single-phase signatures. The nomogram based on a comprehensive model incorporating the clinical risk factors and the fusion radiomic signature was established, and decision curve analysis was applied for clinical use. RESULTS: The fusion radiomic signature has significant association with histologic grade (p < 0.001). The nomogram integrating independent clinical risk factor tumor margin and fusion radiomic signature showed strong discrimination with an area under the curve (AUC) of 0.974 (95% CI 0.950-0.998) in the training cohort and 0.902 (95% CI 0.798-1.000) in the validation cohort with good calibration. Decision curve analysis verified the clinical usefulness of the predictive nomogram. CONCLUSION: We proposed a comprehensive nomogram consisting of tumor margin and fusion radiomic signature as a powerful tool to predict grade 1 and grade 2/3 PNET preoperatively and assist the clinical decision-making for PNET patients. KEY POINTS: • Radiomic signature has strong discriminatory ability for the histologic grade of PNETs. • Arterial and portal venous phase CT imaging are complementary for the prediction of PNET grading. • The comprehensive nomogram outperformed clinical factors in assisting therapy strategy in PNET patients.

Pretreatment prediction of immunoscore in hepatocellular cancer: a radiomics-based clinical model based on Gd-EOB-DTPA-enhanced MRI imaging.

OBJECTIVES: Immunoscore evaluates the density of CD3+ and CD8+ T cells in both the tumor core and invasive margin. Pretreatment prediction of immunoscore in hepatocellular cancer (HCC) is important for precision immunotherapy. We aimed to develop a radiomics model based on gadolinium-ethoxybenzyl-diethylenetriamine (Gd-EOB-DTPA)-enhanced MRI for pretreatment prediction of immunoscore (0-2 vs. 3-4) in HCC. MATERIALS AND METHODS: The study included 207 (training cohort: n = 150; validation cohort: n = 57) HCC patients with hepatectomy who underwent preoperative Gd-EOB-DTPA-enhanced MRI. The volumes of interest enclosing hepatic lesions including intratumoral and peritumoral regions were manually delineated in the hepatobiliary phase of MRI images, from which 1044 quantitative features were extracted and analyzed. Extremely randomized tree method was used to select radiomics features for building radiomics model. Predicting performance in immunoscore was compared among three models: (1) using only intratumoral radiomics features (intratumoral radiomics model); (2) using combined intratumoral and peritumoral radiomics features (combined radiomics model); (3) using clinical data and selected combined radiomics features (combined radiomics-based clinical model). RESULTS: The combined radiomics model showed a better predicting performance in immunoscore than intratumoral radiomics model (AUC, 0.904 (95% CI 0.855-0.953) vs. 0.823 (95% CI 0.747-0.899)). The combined radiomics-based clinical model showed an improvement over the combined radiomics model in predicting immunoscore (AUC, 0·926 (95% CI 0·884-0·967) vs. 0·904 (95% CI 0·855-0·953)), although differences were not statistically significant. Results were confirmed in validation cohort and calibration curves showed good agreement. CONCLUSION: The MRI-based combined radiomics nomogram is effective in predicting immunoscore in HCC and may help making treatment decisions. KEY POINTS: • Radiomics obtained from Gd-EOB-DTPA-enhanced MRI help predicting immunoscore in hepatocellular carcinoma. • Combined intratumoral and peritumoral radiomics are superior to intratumoral radiomics only in predicting immunoscore. • We developed a combined clinical and radiomicsnomogram to predict immunoscore in hepatocellular carcinoma.

A multi-sequence and habitat-based MRI radiomics signature for preoperative prediction of MGMT promoter methylation in astrocytomas with prognostic implication.

OBJECTIVES: Oxygen 6-methylguanine-DNA methyltransferase (MGMT) promoter methylation is a significant prognostic biomarker in astrocytomas, especially for temozolomide (TMZ) chemotherapy. This study aimed to preoperatively predict MGMT methylation status based on magnetic resonance imaging (MRI) radiomics and validate its value for evaluation of TMZ chemotherapy effect. METHODS: We retrospectively reviewed a cohort of 105 patients with grade II-IV astrocytomas. Radiomic features were extracted from the tumour and peritumoral oedema habitats on contrast-enhanced T1-weighted images, T2-weighted fluid-attenuated inversion recovery images and apparent diffusion coefficient (ADC) maps. The following radiomics analysis was structured in three phases: feature reduction, signature construction and discrimination statistics. A fusion radiomics signature was finally developed using logistic regression modelling. Predictive performance was compared between the radiomics signature, previously reported clinical factors and ADC parameters. Validation was additionally performed on a time-independent cohort (n = 31). The prognostic value of the signature on overall survival for TMZ chemotherapy was explored using Kaplan Meier estimation. RESULTS: The fusion radiomics signature exhibited supreme power for predicting MGMT promoter methylation, with area under the curve values of 0.925 in the training cohort and 0.902 in the validation cohort. Performance of the radiomics signature surpassed that of clinical factors and ADC parameters. Moreover, the radiomics approach successfully divided patients into high-risk and low-risk groups for overall survival after TMZ chemotherapy (p = 0.03). CONCLUSIONS: The proposed radiomics signature accurately predicted MGMT promoter methylation in patients with astrocytomas, and achieved survival stratification for TMZ chemotherapy, thus providing a preoperative basis for individualised treatment planning. KEY POINTS: • Radiomics using magnetic resonance imaging can preoperatively perform satisfactory prediction of MGMT methylation in grade II-IV astrocytomas. • Habitat-based radiomics can improve efficacy in predicting MGMT methylation status. • Multi-sequence radiomics signature has the power to evaluate TMZ chemotherapy effect.

Functional significance of Hippo/YAP signaling for drug resistance in colorectal cancer.

Colorectal cancer is a leading cause of cancer-related death worldwide. While early stage colorectal cancer can be removed by surgery, patients with advanced disease are treated by chemotherapy, with 5-Fluorouracil (5-FU) as a main ingredient. However, most patients with advanced colorectal cancer eventually succumb to the disease despite some responded initially. Thus, identifying molecular mechanisms responsible for drug resistance will help design novel strategies to treat colorectal cancer. In this study, we analyzed an acquired 5-FU resistant cell line, LoVo-R, and determined that elevated expression of YAP target genes is a major alteration in the 5-FU resistant cells. Hippo/YAP signaling, a pathway essential for cell polarity, is an important regulator for tissue homeostasis, organ size, and stem cells. We demonstrated that knockdown of YAP1 sensitized LoVo-R cells to 5-FU treatment in cultured cells and in mice. The relevance of our studies to colorectal cancer patients is reflected by our discovery that high expression of YAP target genes in the tumor was associated with an increased risk of cancer relapse and poor survival in a larger cohort of colorectal cancer patients who underwent 5-FU-related chemotherapy. Taken together, we demonstrate a critical role of YAP signaling for drug resistance in colorectal cancer.

Non-invasive genotype prediction of chromosome 1p/19q co-deletion by development and validation of an MRI-based radiomics signature in lower-grade gliomas.

PURPOSE: To perform radiomics analysis for non-invasively predicting chromosome 1p/19q co-deletion in World Health Organization grade II and III (lower-grade) gliomas. METHODS: This retrospective study included 277 patients histopathologically diagnosed with lower-grade glioma. Clinical parameters were recorded for each patient. We performed a radiomics analysis by extracting 647 MRI-based features and applied the random forest algorithm to generate a radiomics signature for predicting 1p/19q co-deletion in the training cohort (n = 184). The clinical model consisted of pertinent clinical factors, and was built using a logistic regression algorithm. A combined model, incorporating both the radiomics signature and related clinical factors, was also constructed. The receiver operating characteristics curve was used to evaluate the predictive performance. We further validated the predictability of the three developed models using a time-independent validation cohort (n = 93). RESULTS: The radiomics signature was constructed as an independent predictor for differentiating 1p/19q co-deletion genotypes, which demonstrated superior performance on both the training and validation cohorts with areas under curve (AUCs) of 0.887 and 0.760, respectively. These results outperformed the clinical model (AUCs of 0.580 and 0.627 on training and validation cohorts). The AUCs of the combined model were 0.885 and 0.753 on training and validation cohorts, respectively, which indicated that clinical factors did not present additional improvement for the prediction. CONCLUSION: Our study highlighted that an MRI-based radiomics signature can effectively identify the 1p/19q co-deletion in histopathologically diagnosed lower-grade gliomas, thereby offering the potential to facilitate non-invasive molecular subtype prediction of gliomas.

FTY720 Abrogates Collagen-Induced Arthritis by Hindering Dendritic Cell Migration to Local Lymph Nodes.

Because dendritic cells (DCs) play critical roles in the pathogenesis of rheumatoid arthritis, modulation of their functions could serve as a novel therapy. In this study, we demonstrated that FTY720 treatment significantly suppressed the incidence and severity of collagen-induced arthritis (CIA) in DBA/1J mice via the modulation of DC functions. In FTY720-treated CIA mice, a decrease in the number of DCs in local draining lymph nodes (LNs) was observed. In vitro, FTY720 inhibited the trafficking of LPS-stimulated bone marrow-derived DCs (BMDCs). Decreased secretion of CCL19 and downregulation of CCR7 on DCs may explain the mechanisms underlying the impairment of DC migration induced by FTY720. In a DC-induced mouse arthritis model, FTY720 treatment also suppressed the incidence and severity of arthritis, which was correlated with a decrease in the migration of injected BMDCs to draining LNs. Although lower levels of costimulatory molecules (CD40, CD80, and CD86) and I-A(q) expressed on LN DCs were observed in FTY720-treated mice, in vitro analysis showed no effect of FTY720 on LPS-stimulated BMDC maturation. Furthermore, LN cells from FTY720-treated CIA mice displayed diminished production of proinflammatory cytokines in response to collagen II and Con A stimulation. In addition, the ratio of Th1/Th2 in the draining LNs of mice with DC-induced arthritis was decreased upon FTY720 treatment. This finding was consistent with the fact that FTY720 suppressed IL-12p70 production in cultured BMDCs. Taken together, these results indicate that inhibition of DC migration by FTY720 may provide a novel approach in treating autoimmune diseases such as rheumatoid arthritis.

A role for transcription factor STAT3 signaling in oncogene smoothened-driven carcinogenesis.

Activation of the Hedgehog (Hh) pathway is known to drive development of basal cell carcinoma and medulloblastomas and to associate with many other types of cancer, but the exact molecular mechanisms underlying the carcinogenesis process remain elusive. We discovered that skin tumors derived from epidermal expression of oncogenic Smo, SmoM2, have elevated levels of IL-11, IL-11Rα, and STAT3 phosphorylation at Tyr(705). The relevance of our data to human conditions was reflected by the fact that all human basal cell carcinomas examined have detectable STAT3 phosphorylation, mostly in keratinocytes. The functional relevance of STAT3 in Smo-mediated carcinogenesis was revealed by epidermal specific knockout of STAT3. We showed that removal of STAT3 from mouse epidermis dramatically reduced SmoM2-mediated cell proliferation, leading to a significant decrease in epidermal thickness and tumor development. We also observed a significant reduction of epidermal stem/progenitor cell population and cyclin D1 expression in mice with epidermis-specific knockout of STAT3. Our evidence indicates that STAT3 signaling activation may be mediated by the IL-11/IL-11Rα signaling axis. We showed that tumor development was reduced after induced expression of SmoM2 in IL-11Rα null mice. Similarly, neutralizing antibodies for IL-11 reduced the tumor size. In two Hh-responsive cell lines, ES14 and C3H10T1/2, we found that addition of Smo agonist purmorphamine is sufficient to induce STAT3 phosphorylation at Tyr(705), but this effect was abolished after IL-11Rα down-regulation by shRNAs. Taken together, our results support an important role of the IL-11Rα/STAT3 signaling axis for Hh signaling-mediated signaling and carcinogenesis.

PTPN22 -1123G > C polymorphism is associated with susceptibility to primary immune thrombocytopenia in Chinese population.

Primary immune thrombocytopenia (ITP) is an acquired autoimmune disorder characterized by autoantibody-mediated platelet destruction. Multiple factors have been implicated in ITP pathogenesis, including T-lymphocyte dysfunctions. The protein tyrosine phosphatase, non-receptor type 22 (PTPN22) gene encodes lymphoid-specific phosphatase (LYP), a critical negative regulator of T cell activation. Single nucleotide polymorphisms (SNPs) of PTPN22 have been broadly associated with susceptibilities to various autoimmune disorders. Here we conducted a case-control study investigating whether the PTPN22 -1123G > C SNP contributes to the risk of ITP in Chinese population. The study included 191 ITP cases and 216 ethnically matched normal controls. Genotyping of -1123G > C SNP was performed using a single-base extension (SBE) and mass spectrometry method. Allelic and genotypic frequencies were compared between the case-control groups by the chi-square test. We observed significant overrepresentation of -1123G allele (p = 0.034, odds ratio (OR) = 1.374, 95% confidence interval (CI) [1.024-1.843]) and GG genotype (P = 0.038, OR = 1.951, 95% CI [1.031-3.694]) in the patients compared with the controls. Stratified analysis by gender and age of disease onset revealed comparable observations in both male and adult ITP cohorts. These data suggest a moderate association of PTPN22 -1123G > C SNP with susceptibility to ITP. Together with previous reports, our finding provides further evidence for PTPN22 being a general autoimmunity gene.

A multi-scale, multi-region and attention mechanism-based deep learning framework for prediction of grading in hepatocellular carcinoma.

BACKGROUND: Histopathological grading is a significant risk factor for postsurgical recurrence in hepatocellular carcinoma (HCC). Preoperative knowledge of histopathological grading could provide instructive guidance for individualized treatment decision-making in HCC management. PURPOSE: This study aims to develop and validate a newly proposed deep learning model to predict histopathological grading in HCC with improved accuracy. METHODS: In this dual-centre study, we retrospectively enrolled 384 HCC patients with complete clinical, pathological and radiological data. Aiming to synthesize radiological information derived from both tumour parenchyma and peritumoral microenvironment regions, a modelling strategy based on a multi-scale and multi-region dense connected convolutional neural network (MSMR-DenseCNNs) was proposed to predict histopathological grading using preoperative contrast enhanced computed tomography (CT) images. Multi-scale inputs were defined as three-scale enlargement of an original minimum bounding box in width and height by given pixels, which correspondingly contained more peritumoral analysis areas with the enlargement. Multi-region inputs were defined as three regions of interest (ROIs) including a squared ROI, a precisely delineated tumour ROI, and a peritumoral tissue ROI. The DenseCNN structure was designed to consist of a shallow feature extraction layer, dense block module, and transition and attention module. The proposed MSMR-DenseCNN was pretrained by the ImageNet dataset to capture basic graphic characteristics from the images and was retrained by the collected retrospective CT images. The predictive ability of the MSMR-DenseCNN models on triphasic images was compared with a conventional radiomics model, radiological model and clinical model. RESULTS: MSMR-DenseCNN applied to the delayed phase (DP) achieved the highest area under the curve (AUC) of 0.867 in the validation cohort for grading prediction, outperforming those on the arterial phase (AP) and portal venous phase (PVP). Fusion of the results on triphasic images did not increase the predictive ability, which underscored the role of DP for grading prediction. Compared with a single-scale and single-region network, the DP-phase based MSMR-DenseCNN model remarkably raised sensitivity from 67.4% to 75.5% with comparable specificity of 78.6%. MSMR-DenseCNN on DP defeated conventional radiomics, radiological and clinical models, where the AUCs were correspondingly 0.765, 0.695 and 0.612 in the validation cohort. CONCLUSIONS: The MSMR-DenseCNN modelling strategy increased the accuracy for preoperative prediction of grading in HCC, and enlightens similar radiological analysis pipelines in a variety of clinical scenarios in HCC management.