Methyladenosine modifications are the most abundant RNA modifications, including N6-methyladenosine (m6A), N1-methyladenosine (m1A), and 2’-O-methyladenosine (m6Am). As reversible epigenetic ...modifications, methyladenosine modifications in eukaryotic RNAs are not invariable. Drastic alterations of m6A are found in a variety of diseases, including cancers. Dynamic changes of m6A modification induced by abnormal methyltransferase, demethylases, and readers can regulate cancer progression via interfering with the splicing, localization, translation, and stability of mRNAs. Meanwhile, m6A, m1A, and m6Am modifications also exert regulatory effects on noncoding RNAs in cancer progression. In this paper, we reviewed recent findings concerning the underlying biomechanism of methyladenosine modifications in oncogenesis and metastasis and discussed the therapeutic potential of methyladenosine modifications in cancer treatments.
A strong correlation between NOS2 and COX2 tumor expression and poor clinical outcomes in ER breast cancer has been established. However, the mechanisms of tumor induction of these enzymes are ...unclear. Analysis of The Cancer Genome Atlas (TCGA) revealed correlations between NOS2 and COX2 expression and Th1 cytokines. Herein, single-cell RNAseq analysis of TNBC cells shows potent NOS2 and COX2 induction by IFNγ combined with IL1β or TNFα. Given that IFNγ is secreted by cytolytic lymphocytes, which improve clinical outcomes, this role of IFNγ presents a dichotomy. To explore this conundrum, tumor NOS2, COX2, and CD8
T cells were spatially analyzed in aggressive ER-, TNBC, and HER2 + breast tumors. High expression and clustering of NOS2-expressing tumor cells occurred at the tumor/stroma interface in the presence of stroma-restricted CD8
T cells. High expression and clustering of COX2-expressing tumor cells extended into immune desert regions in the tumor core where CD8
T cell penetration was limited or absent. Moreover, high NOS2-expressing tumor cells were proximal to areas with increased satellitosis, suggestive of cell clusters with a higher metastatic potential. Further in vitro experiments revealed that IFNγ + IL1β/TNFα increased the elongation and migration of treated tumor cells. This spatial analysis of the tumor microenvironment provides important insight into distinct neighborhoods where stroma-restricted CD8
T cells exist proximal to NOS2-expressing tumor niches that could have increased metastatic potential.
Clinician's Corner represents the opinions and views of the author and does not reflect any policy or opinion of the American Cancer Society, Cancer Cytopathology, or Wiley unless this is clearly ...specified.
•It is the first work using deep learning method on liver transplantation patients with Hepatocellular Carcinoma to predict survival outcomes.•The novel imageomic model combines clinical data with ...image features to predict recurrence risk in liver transplantation recipients.•We successfully validated that the model can discover risk factors for recurrence other than tumor size and biomarker analysis.•This model may alter the liver transplantation allocation system for Hepatocellular Carcinoma patients with the highest tumor burden.
Liver transplantation (LT) is an effective treatment for hepatocellular carcinoma (HCC), the most common type of primary liver cancer. Patients with small HCC (<5 cm) are given priority over others for transplantation due to clinical allocation policies based on tumor size. Attempting to shift from the prevalent paradigm that successful transplantation and longer disease-free survival can only be achieved in patients with small HCC to expanding the transplantation option to patients with HCC of the highest tumor burden (>5 cm), we developed a convergent artificial intelligence (AI) model that combines transient clinical data with quantitative histologic and radiomic features for more objective risk assessment of liver transplantation for HCC patients.
Patients who received a LT for HCC between 2008–2019 were eligible for inclusion in the analysis. All patients with post-LT recurrence were included, and those without recurrence were randomly selected for inclusion in the deep learning model. Pre- and post-transplant magnetic resonance imaging (MRI) scans and reports were compressed using CapsNet networks and natural language processing, respectively, as input for a multiple feature radial basis function network. We applied a histological image analysis algorithm to detect pathologic areas of interest from explant tissue of patients who recurred. The multilayer perceptron was designed as a feed-forward, supervised neural network topology, with the final assessment of recurrence risk. We used area under the curve (AUC) and F-1 score to assess the predictability of different network combinations.
A total of 109 patients were included (87 in the training group, 22 in the testing group), of which 20 were positive for cancer recurrence. Seven models (AUC; F-1 score) were generated, including clinical features only (0.55; 0.52), magnetic resonance imaging (MRI) only (0.64; 0.61), pathological images only (0.64; 0.61), MRI plus pathology (0.68; 0.65), MRI plus clinical (0.78, 0.75), pathology plus clinical (0.77; 0.73), and a combination of clinical, MRI, and pathology features (0.87; 0.84). The final combined model showed 80 % recall and 89 % precision. The total accuracy of the implemented model was 82 %.
We validated that the deep learning model combining clinical features and multi-scale histopathologic and radiomic image features can be used to discover risk factors for recurrence beyond tumor size and biomarker analysis. Such a predictive, convergent AI model has the potential to alter the LT allocation system for HCC patients and expand the transplantation treatment option to patients with HCC of the highest tumor burden.
We present a method for automated brain tissue segmentation based on the multi-channel fusion of diffusion tensor imaging (DTI) data. The method is motivated by the evidence that independent tissue ...segmentation based on DTI parametric images provides complementary information of tissue contrast to the tissue segmentation based on structural MRI data. This has important applications in defining accurate tissue maps when fusing structural data with diffusion data. In the absence of structural data, tissue segmentation based on DTI data provides an alternative means to obtain brain tissue segmentation. Our approach to the tissue segmentation based on DTI data is to classify the brain into two compartments by utilizing the tissue contrast existing in a single channel. Specifically, because the apparent diffusion coefficient (ADC) values in the cerebrospinal fluid (CSF) are more than twice that of gray matter (GM) and white matter (WM), we use ADC images to distinguish CSF and non-CSF tissues. Additionally, fractional anisotropy (FA) images are used to separate WM from non-WM tissues, as highly directional white matter structures have much larger fractional anisotropy values. Moreover, other channels to separate tissue are explored, such as eigenvalues of the tensor, relative anisotropy (RA), and volume ratio (VR). We developed an approach based on the Simultaneous Truth and Performance Level Estimation (STAPLE) algorithm that combines these two-class maps to obtain a complete tissue segmentation map of CSF, GM, and WM. Evaluations are provided to demonstrate the performance of our approach. Experimental results of applying this approach to brain tissue segmentation and deformable registration of DTI data and spoiled gradient-echo (SPGR) data are also provided.
The goal of this study is to explore the pharmacological potential of the amyloid-reducing vasodilator fasudil, a selective Ras homolog (Rho)-associated kinases (ROCK) inhibitor, in the P301S tau ...transgenic mouse model (Line PS19) of neurodegenerative tauopathy and Alzheimer's disease (AD).
We used LC-MS/MS, ELISA and bioinformatic approaches to investigate the effect of treatment with fasudil on the brain proteomic profile in PS19 tau transgenic mice. We also explored the efficacy of fasudil in reducing tau phosphorylation, and the potential beneficial and/or toxic effects of its administration in mice.
Proteomic profiling of mice brains exposed to fasudil revealed the activation of the mitochondrial tricarboxylic acid (TCA) cycle and blood-brain barrier (BBB) gap junction metabolic pathways. We also observed a significant negative correlation between the brain levels of phosphorylated tau (pTau) at residue 396 and both fasudil and its metabolite hydroxyfasudil.
Our results provide evidence on the activation of proteins and pathways related to mitochondria and BBB functions by fasudil treatment and support its further development and therapeutic potential for AD.
Background
Patients receiving cranial radiation face the risk of delayed brain dysfunction. However, an early medical imaging marker is not available until irreversible morphological changes emerge.
...Purpose
To explore the micromorphological white matter changes during the radiotherapy session by utilizing an along‐tract analysis framework.
Study Type
Prospective.
Population
Eighteen nasopharyngeal carcinoma (two female) patients receiving cranial radiation.
Field Strength/Sequence
3.0 T; Diffusion tensor imaging (DTI) and T1‐ and T2‐weighted images (T1W, T2W); computed tomography (CT).
Assessment
Patients received three DTI imaging scans during the radiotherapy (RT), namely the baseline scan (1–2 days before RT began), the middle scan (the middle of the RT session), and the end scan (1–2 days after RT ended). Twelve fibers were segmented after whole‐brain tractography. Then, the fractional anisotropy (FA) values and the cumulative radiation dose received for each fiber streamline were resampled and projected into their center fiber.
Statistical Tests
The contrast among the three scans (P1: middle scan–baseline scan; P2: end scan–middle scan; P3: end scan–baseline scan) were compared using the linear mixed model for each of the 12 center fibers. Then, a dose–responsiveness relationship was performed using Pearson correlation. P < 0.05 was considered statistically significant.
Results
Six of the 12 center fibers showed significant changes of FA values during the RT but with heterogeneous patterns. The significant changes along a specific center fiber were associated with their cumulative dose received (Genu: P1 r = −0.6182, P2 r = −0.5907; Splenium: P1 r = 0.4055, P = 0.1063, P2 r = 0.6742; right uncinate fasciculus: P1 r = −0.3865, P2 r = −0.4912, P = 0.0533; right corticospinal tract: P1 r = 0.4273, P = 0.1122, P2 r = −0.6885).
Data Conclusion
The along‐tract analysis might provide sensitive measures on the early‐onset micromorphological changes.
Level of Evidence
2
Technical Efficacy
Stage 3
Subarachnoid hemorrhage (SAH) in 95% of cases results in long-term disabilities due to brain damage, pathogenesis of which remains uncertain. Hindrance of cerebrospinal fluid (CSF) circulation along ...glymphatic pathways is a possible mechanism interrupting drainage of damaging substances from subarachnoid space and parenchyma. We explored changes in CSF circulation at different time following SAH and possible role of brain tissue factor (TF). Fluorescent solute and fluorescent microspheres injected into cisterna magna were used to track CSF flow in mice. SAH induced by perforation of circle of Willis interrupted CSF flow for up to 30 days. Block of CSF flow did not correlate with the size of hemorrhage. Following SAH, fibrin deposits were observed on the brain surface including areas without visible blood. Block of astroglia-associated TF by intracerebroventricular administration of specific antibodies increased size of hemorrhage, decreased fibrin deposition and facilitated spread of fluorophores in sham/naïve animals. We conclude that brain TF plays an important role in localization of hemorrhage and also regulates CSF flow under normal conditions. Targeting of the TF system will allow developing of new therapeutic approaches to the treatment of SAH and pathologies related to CSF flow such as hydrocephalus.
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