A combination of intrinsic and extrinsic features improves prognostic prediction in malignant pleural mesothelioma.

BACKGROUND: Malignant pleural mesothelioma (MPM) is a lung pleural cancer with very poor disease outcome. With limited curative MPM treatment available, it is vital to study prognostic biomarkers to categorise different patient risk groups. METHODS: We defined gene signatures to separately characterise intrinsic and extrinsic features, and investigated their interactions in MPM tumour samples. Specifically, we calculated gene signature scores to capture the downstream pathways of major mutated driver genes (BAP1, NF2, SETD2 and TP53) as tumour-intrinsic features. Similarly, we inferred the infiltration levels for major immune cells in the tumour microenvironment to characterise tumour-extrinsic features. Lastly, we integrated these features with clinical factors to predict prognosis in MPM. RESULTS: The gene signature scores were more prognostic than the corresponding genomic mutations, mRNA and protein expression. High immune infiltration levels were associated with prolonged survival. The integrative model indicated that tumour features provided independent prognostic values than clinical factors and were complementary with each other in survival prediction. CONCLUSIONS: By using an integrative model that combines intrinsic and extrinsic features, we can more correctly predict the clinical outcomes of patients with MPM.

Copper encapsulated in grass-derived phytoliths: Characterization, dissolution properties and the relation of content to soil properties.

The formation of phytoliths as a result of the precipitation of Si in many Si-rich plant species is known to encapsulate organic matter. This work aims to examine the possible encapsulation of Cu in grass phytoliths in an orange growing area, where Cu-rich fungicides have been excessively applied. Batch experiments, in combination with SEM-EDS and microscopy, were conducted for the grass-derived phytoliths and phytoliths separated from soil, thus revealing their dissolution properties, morphotypes and contents, in relation to soil properties. By measuring the Cu release accompanying the dissolution of phytoliths by different extractants, especially an Na2CO3/HNO3 solution, it was revealed that Cu was encapsulated within the silica body of the phytolith. This sink of Cu in the grass can be cycled to serve as a new Cu source in soils. Phytolith contents in the soil were up to 17.7 g kg-1 and tended to accumulate in soil depths from 0 to 20 cm. A positive correlation was found for soil phytolith and phytCu contents and may be indicative of the role of phytoliths as an enhancer of Cu accumulation in soil. It would be worth developing suitable techniques for the determination of phytCu, because common extraction/digestion methods are not suited for evaluating this Cu pool.

Cdx2 Regulates Gene Expression through Recruitment of Brg1-associated Switch-Sucrose Non-fermentable (SWI-SNF) Chromatin Remodeling Activity.

The packaging of genomic DNA into nucleosomes creates a barrier to transcription that can be relieved through ATP-dependent chromatin remodeling via complexes such as the switch-sucrose non-fermentable (SWI-SNF) chromatin remodeling complex. The SWI-SNF complex remodels chromatin via conformational or positional changes of nucleosomes, thereby altering the access of transcriptional machinery to target genes. The SWI-SNF complex has limited ability to bind to sequence-specific elements, and, therefore, its recruitment to target loci is believed to require interaction with DNA-associated transcription factors. The Cdx family of homeodomain transcript ion factors (Cdx1, Cdx2, and Cdx4) are essential for a number of developmental programs in the mouse. Cdx1 and Cdx2 also regulate intestinal homeostasis throughout life. Although a number of Cdx target genes have been identified, the basis by which Cdx members impact their transcription is poorly understood. We have found that Cdx members interact with the SWI-SNF complex and make direct contact with Brg1, a catalytic member of SWI-SNF. Both Cdx2 and Brg1 co-occupy a number of Cdx target genes, and both factors are necessary for transcriptional regulation of such targets. Finally, Cdx2 and Brg1 occupancy occurs coincident with chromatin remodeling at some of these loci. Taken together, our findings suggest that Cdx transcription factors regulate target gene expression, in part, through recruitment of Brg1-associated SWI-SNF chromatin remodeling activity.

Clonal gene signatures predict prognosis in mesothelioma and lung adenocarcinoma.

Malignant pleural mesothelioma (MPM) is a rare but lethal pleural cancer with high intratumor heterogeneity (ITH). A recent study in lung adenocarcinoma has developed a clonal gene signature (ORACLE) from multiregional transcriptomic data and demonstrated high prognostic values and reproducibility. However, such a strategy has not been tested in other types of cancer with high ITH. We aimed to identify biomarkers from multi-regional data to prognostically stratify MPM patients. We generated a multiregional RNA-seq dataset for 78 tumor samples obtained from 26 MPM patients, each with one sample collected from a superior, lateral, and inferior region of the tumor. By integrating this dataset with the Cancer Genome Atlas MPM RNA-seq data, we selected 29 prognostic genes displaying high variability across different tumors but low ITH, which named PRACME (Prognostic Risk Associated Clonal Mesothelioma Expression). We evaluated PRACME in two independent MPM datasets and demonstrated its prognostic values. Patients with high signature scores are associated with poor prognosis after adjusting established clinical factors. Interestingly, the PRACME and the ORACLE signatures defined respectively from MPM and lung adenocarcinoma cross-predict prognosis between the two cancer types. Further investigation indicated that the cross-prediction ability might be explained by the high similarity between the two cancer types in their genomic regions with copy number variation, which host many clonal genes. Overall, our clonal signature PRACME provided prognostic stratification in MPM and this study emphasized the importance of multi-regional transcriptomic data for prognostic stratification based on clonal genes.

Adjuvant 5-Fluorouracil/leucovorin, capecitabine, and oxaliplatin-related regimens for stage II/III colon cancer patients 66 years or older.

Adjuvant chemotherapy of leucovorin-modulated 5-fluorouracil (5-FU/LV), capecitabine, and adding oxaliplatin to 5-FU/LV or capecitabine (FLOX/OX) have been standard regimens for high-risk stage II or III colon cancer (CC). We aimed to evaluate their patterns of use, association with survival, and rate of emergency room visit (ER) or hospitalization during the treatment period. High-risk stage II or III patients aged >65 years diagnosed between 2007 and 2015, underwent colectomy, and received any of these three regimens were selected from SEER and Texas Cancer Registry (TC) linked with Medicare data. Chi-square test, Kaplan-Meier survival curves, Cox regression, and logistic regression were used in data analysis. A total of 5621 (1080 stage II and 4541 stage III) patients with median age of 72 years were included in this study. For stage II, 24.4% used 5-FU/LV, 31.2% used capecitabine, and 44.4% used FLOX/OX; the respective numbers for stage III were 13.8%, 17.9%, and 68.3%. Patients aged <70 years, not in the West region, not in Medicare state-buy-in program, and with no comorbidity were more likely to use FLOX/OX. FLOX/OX was associated with improved overall survival (OS) in stage II and III patients and improved cancer-specific survival in stage III patients compared with 5-FU/LV. The survival benefit of FLOX/OX was sustained in stage III patients aged ≥70 years. Capecitabine had the lowest ER/hospitalization rate with 19.2% in stage II and 28.9% in III. The use of FLOX/OX was associated with improved survival compared with 5-FU/LV among CC patients. Capecitabine was associated with the lowest ER/hospitalization rate.

Immune Infiltration in Tumor and Adjacent Non-Neoplastic Regions Codetermines Patient Clinical Outcomes in Early-Stage Lung Cancer.

INTRODUCTION: In recent years, the proportion of patients with NSCLC diagnosed at an early stage has increased continuously. METHODS: In this study, we analyzed samples and data collected from 119 samples from 67 early stage patients with NSCLC, including 52 pairs of tumor and adjacent non-neoplastic samples, and performed RNA-sequencing analysis with high sequencing depth. RESULTS: We found that immune-related genes were highly enriched among the differentially expressed genes and observed significantly higher inferred immune infiltration levels in adjacent non-neoplastic samples than in tumor samples. In survival analysis, the infiltration of certain immune cell types in tumor, but not adjacent non-neoplastic, samples were associated with overall patient survival, and excitingly, the differential infiltration between paired samples (tumor minus non-neoplastic) was more prognostic than expression in either non-neoplastic or tumor tissues. We also performed B cell receptor (BCR) and T cell receptor (TCR) repertoire analysis and observed more BCR/TCR clonotypes and increased BCR clonality in tumor than in non-neoplastic samples. Finally, we carefully quantified the fraction of the five histologic subtypes in our adenocarcinoma samples and found that higher histologic pattern complexity was associated with higher immune infiltration and low TCR clonality in the tumor-proximal regions. CONCLUSIONS: Our results indicated significantly differential immune characteristics between tumor and adjacent non-neoplastic samples and suggested that the two regions provided complementary prognostic values in early-stage NSCLCs.

BeCaked: An Explainable Artificial Intelligence Model for COVID-19 Forecasting.

From the end of 2019, one of the most serious and largest spread pandemics occurred in Wuhan (China) named Coronavirus (COVID-19). As reported by the World Health Organization, there are currently more than 100 million infectious cases with an average mortality rate of about five percent all over the world. To avoid serious consequences on people's lives and the economy, policies and actions need to be suitably made in time. To do that, the authorities need to know the future trend in the development process of this pandemic. This is the reason why forecasting models play an important role in controlling the pandemic situation. However, the behavior of this pandemic is extremely complicated and difficult to be analyzed, so that an effective model is not only considered on accurate forecasting results but also the explainable capability for human experts to take action pro-actively. With the recent advancement of Artificial Intelligence (AI) techniques, the emerging Deep Learning (DL) models have been proving highly effective when forecasting this pandemic future from the huge historical data. However, the main weakness of DL models is lacking the explanation capabilities. To overcome this limitation, we introduce a novel combination of the Susceptible-Infectious-Recovered-Deceased (SIRD) compartmental model and Variational Autoencoder (VAE) neural network known as BeCaked. With pandemic data provided by the Johns Hopkins University Center for Systems Science and Engineering, our model achieves 0.98 [Formula: see text] and 0.012 MAPE at world level with 31-step forecast and up to 0.99 [Formula: see text] and 0.0026 MAPE at country level with 15-step forecast on predicting daily infectious cases. Not only enjoying high accuracy, but BeCaked also offers useful justifications for its results based on the parameters of the SIRD model. Therefore, BeCaked can be used as a reference for authorities or medical experts to make on time right decisions.

A lepidic gene signature predicts patient prognosis and sensitivity to immunotherapy in lung adenocarcinoma.

BACKGROUND: Lung adenocarcinoma, the most common type of lung cancer, has a high level of morphologic heterogeneity and is composed of tumor cells of multiple histological subtypes. It has been reported that immune cell infiltration significantly impacts clinical outcomes of patients with lung adenocarcinoma. However, it is unclear whether histologic subtyping can reflect the tumor immune microenvironment, and whether histologic subtyping can be applied for therapeutic stratification of the current standard of care. METHODS: We inferred immune cell infiltration levels using a histological subtype-specific gene expression dataset. From differential gene expression analysis between different histological subtypes, we developed two gene signatures to computationally determine the relative abundance of lepidic and solid components (denoted as the L-score and S-score, respectively) in lung adenocarcinoma samples. These signatures enabled us to investigate the relationship between histological composition and clinical outcomes in lung adenocarcinoma using previously published datasets. RESULTS: We found dramatic immunological differences among histological subtypes. Differential gene expression analysis showed that the lepidic and solid subtypes could be differentiated based on their gene expression patterns while the other subtypes shared similar gene expression patterns. Our results indicated that higher L-scores were associated with prolonged survival, and higher S-scores were associated with shortened survival. L-scores and S-scores were also correlated with global genomic features such as tumor mutation burdens and driver genomic events. Interestingly, we observed significantly decreased L-scores and increased S-scores in lung adenocarcinoma samples with EGFR gene amplification but not in samples with EGFR gene mutations. In lung cancer cell lines, we observed significant correlations between L-scores and cell sensitivity to a number of targeted drugs including EGFR inhibitors. Moreover, lung cancer patients with higher L-scores were more likely to benefit from immune checkpoint blockade therapy. CONCLUSIONS: Our findings provided further insights into evaluating histology composition in lung adenocarcinoma. The established signatures reflected that lepidic and solid subtypes in lung adenocarcinoma would be associated with prognosis, genomic features, and responses to targeted therapy and immunotherapy. The signatures therefore suggested potential clinical translation in predicting patient survival and treatment responses. In addition, our framework can be applied to other types of cancer with heterogeneous histological subtypes.

Markerless tracking for augmented reality for image-guided Endoscopic Retrograde Cholangiopancreatography.

This paper proposes a markerless tracking method with adaptive pose estimation for augmenting 3D organ models on top of the endoscopic image for Endoscopic Retrograde Cholangiopancreatography (ERCP). While many applications of augmented reality (AR) to surgeries need special markers to track the camera's position and orientation in the live video, our method employs the feature detection techniques to track the endoscopic camera. One of the most difficult problems when applying feature-based method to AR for ERCP is the lack of texture & highly specular reflection surface of duodenum in the endoscopic images, which does not provide a stable number of keypoints to track in the endoscopic video sequence. By introducing an adaptive weight function in the combination of reference-current frame tracking with previous-current frame tracking, we enhance the tracking performance remarkably. The proposed method is evaluated using an endoscopic video of a real ERCP and 3D duodenum model reconstructed from CT data of the patient. The result shows real-time performance and robustness of the method.