Novel structural co-expression analysis linking the NPM1-associated ribosomal biogenesis network to chronic myelogenous leukemia

Use of pain-related gene features to predict depression by support vector machine model in patients with fibromyalgia

The prevalence rate of depression is higher in patients with fibromyalgia syndrome, but this is often unrecognized in patients with chronic pain. Given that depression is a common major barrier in the management of patients with fibromyalgia syndrome, an objective tool that reliably predicts depression in patients with fibromyalgia syndrome could significantly enhance the diagnostic accuracy. Since pain and depression can cause each other and worsen each other, we wonder if pain-related genes can be used to differentiate between those with major depression from those without. This study developed a support vector machine model combined with principal component analysis to differentiate major depression in fibromyalgia syndrome patients using a microarray dataset, including 25 fibromyalgia syndrome patients with major depression, and 36 patients without major depression. Gene co-expression analysis was used to select gene features to construct support vector machine model. The principal component analysis can help reduce the number of data dimensions without much loss of information, and identify patterns in data easily. The 61 samples available in the database were not enough for learning based methods and cannot represent every possible variation of each patient. To address this issue, we adopted Gaussian noise to generate a large amount of simulated data for training and testing of the model. The ability of support vector machine model to differentiate major depression using microarray data was measured as accuracy. Different structural co-expression patterns were identified for 114 genes involved in pain signaling pathway by two-sample KS test (p < 0.001 for the maximum deviation D = 0.11 > Dcritical = 0.05), indicating the aberrant co-expression patterns in fibromyalgia syndrome patients. Twenty hub gene features were further selected based on co-expression analysis to construct the model. The principal component analysis reduced the dimension of the training samples from 20 to 16, since 16 components were needed to retain more than 90% of the original variance. The support vector machine model was able to differentiate between those with major depression from those without in fibromyalgia syndrome patients with an average accuracy of 93.22% based on the expression levels of the selected hub gene features. These findings would contribute key information that can be used to develop a clinical decision-making tool for the data-driven, personalized optimization of diagnosing depression in patients with fibromyalgia syndrome.

Augmented Features Synergize Radiomics in Post-Operative Survival Prediction and Adjuvant Therapy Recommendation for Non-Small Cell Lung Cancer

Background

Owing to the cytotoxic effect, it is challenging for clinicians to decide whether post-operative adjuvant therapy is appropriate for a non-small cell lung cancer (NSCLC) patient. Radiomics has proven its promising ability in predicting survival but research on its actionable model, particularly for supporting the decision of adjuvant therapy, is limited.

Methods

Pre-operative contrast-enhanced CT images of 123 NSCLC cases were collected, including 76, 13, 16, and 18 cases from R01 and AMC cohorts of The Cancer Imaging Archive (TCIA), Jiangxi Cancer Hospital and Guangdong Provincial People’s Hospital respectively. From each tumor region, 851 radiomic features were extracted and two augmented features were derived therewith to estimate the likelihood of adjuvant therapy. Both Cox regression and machine learning models with the selected main and interaction effects of 853 features were trained using 76 cases from R01 cohort, and their test performances on survival prediction were compared using 47 cases from the AMC cohort and two hospitals. For those cases where adjuvant therapy was unnecessary, recommendations on adjuvant therapy were made again by the outperforming model and compared with those by IBM Watson for Oncology (WFO).

Results

The Cox model outperformed the machine learning model in predicting survival on the test set (C-Index: 0.765 vs. 0.675). The Cox model consists of 5 predictors, interestingly 4 of which are interactions with augmented features facilitating the modulation of adjuvant therapy option. While WFO recommended no adjuvant therapy for only 13.6% of cases that received unnecessary adjuvant therapy, the same recommendations by the identified Cox model were extended to 54.5% of cases (McNemar’s test p = 0.0003).

Conclusions

A Cox model with radiomic and augmented features could predict survival accurately and support the decision of adjuvant therapy for bettering the benefit of NSCLC patients.

NPM1 Mutated, BCR-ABL1 Positive Myeloid Neoplasms: Review of the Literature

Breakpoint cluster region - Abelson (BCR-ABL1) chimeric protein and mutated Nucleophosmin (NPM1) are often present in hematological cancers, but they rarely coexist in the same disease. Both anomalies are considered founder mutations that inhibit differentiation and apoptosis, but BCR-ABL1 could act as a secondary mutation conferring a proliferative advantage to a pre-neoplastic clone. The 2016 World Health Organization (WHO) classification lists the provisional acute myeloid leukemia (AML) with BCR-ABL1, which must be diagnosed differentially from the rare blast phase (BP) onset of chronic myeloid leukemia (CML), mainly because of the different therapeutic approach in the use of tyrosine kinase inhibitors (TKI). Here we review the BCR/ABL1 plus NPMc+ published cases since 1975 and describe a case from our institution in order to discuss the clinical and molecular features of this rare combination, and report the latest acquisition about an occurrence that could pertain either to the rare AML BCR-ABL1 positive or the even rarer CML-BP with mutated NPM1 at the onset. Differential diagnosis is based on careful analysis of genotypic and phenotypic features and anamnestic, clinical evolution, and background data. Therapeutic decisions must consider the broader clinical aspects, the comparatively mild effects of TKI therapy versus the great benefit that might bring to most of the patients, as may be incidentally demonstrated by our case history.

Identification of Reproducible BCL11A Alterations in Schizophrenia Through Individual-Level Prediction of Coexpression

Previous studies have provided evidence for an alteration of genetic coexpression in schizophrenia (SCZ). However, such analyses have thus far lacked biological specificity for individual genes, which may be critical for identifying illness-relevant effects. Therefore, we applied machine learning to identify gene-specific coexpression differences at the individual subject level and compared these between individuals with SCZ, bipolar disorder, major depressive disorder (MDD), autism spectrum disorder (ASD), and healthy controls. Utilizing transcriptome-wide gene expression data from 21 independent datasets, comprising a total of 9509 participants, we identified a reproducible decrease of BCL11A coexpression across 4 SCZ datasets that showed diagnostic specificity for SCZ when compared with ASD and MDD. We further demonstrate that individual-level coexpression differences can be combined in multivariate coexpression scores that show reproducible illness classification across independent datasets in SCZ and ASD. This study demonstrates that machine learning can capture gene-specific coexpression differences at the individual subject level for SCZ and identify novel biomarker candidates.

Nucleophosmin in leukemia: Consequences of anchor loss

Nucleophosmin (NPM), one of the most abundant nucleolar proteins, has crucial functions in ribosome biogenesis, cell cycle control, and DNA-damage repair. In human cells, NPM occurs mainly in oligomers. It functions as a chaperone, undergoes numerous interactions and forms part of many protein complexes. Although NPM role in carcinogenesis is not fully elucidated, a variety of tumor suppressor as well as oncogenic activities were described. NPM is overexpressed, fused with other proteins, or mutated in various tumor types. In the acute myeloid leukemia (AML), characteristic mutations in NPM1 gene, leading to modification of NPM C-terminus, are the most frequent genetic aberration. Although multiple mutation types of NPM are found in AML, they are all characterized by aberrant cytoplasmic localization of the mutated protein. In this review, current knowledge of the structure and function of NPM is presented in relation to its interaction network, in particular to the interaction with other nucleolar proteins and with proteins active in apoptosis. Possible molecular mechanisms of NPM mutation-driven leukemogenesis and NPM therapeutic targeting are discussed. Finally, recent findings concerning the immunogenicity of the mutated NPM and specific immunological features of AML patients with NPM mutation are summarized.

Treating hematological malignancies with drugs inhibiting ribosome biogenesis: when and why

It is well known that chemotherapy can cure only some cancers in advanced stage, mostly those with an intact p53 pathway. Hematological cancers such as lymphoma and certain forms of leukemia are paradigmatic examples of such scenario. Recent evidence indicates that the efficacy of many of the alkylating and intercalating agents, antimetabolites, topoisomerase, and kinase inhibitors used in cancer therapy is largely due to p53 stabilization and activation consequent to the inhibition of ribosome biogenesis. In this context, innovative drugs specifically hindering ribosome biogenesis showed preclinical activity and are currently in early clinical development in hematological malignancies. The mechanism of p53 stabilization after ribosome biogenesis inhibition is a multistep process, depending on specific factors that can be altered in tumor cells, which can affect the antitumor efficacy of ribosome biogenesis inhibitors (RiBi). In the present review, the basic mechanisms underlying the anticancer activity of RiBi are discussed based on the evidence deriving from available preclinical and clinical studies, with the purpose of defining when and why the treatment with drugs inhibiting ribosomal biogenesis could be highly effective in hematological malignancies.

Network-based expression analyses and experimental validations revealed high co-expression between Yap1 and stem cell markers compared to differentiated cells

The Hippo signaling pathway is identified as a potential regulatory pathway which plays critical roles in differentiation and stem cell self-renewal. Yap1 is a primary transcriptional effector of this pathway. The importance of Yap1 in embryonic stem cells (ESCs) and differentiation procedure remains a challenging question, since two different observations have been reported. To answer this question we used co-expression network and differential co-expression analyses followed by experimental validations. Our results indicate that Yap1 is highly co-expressed with stem cell markers in ESCs but not in differentiated cells (DCs). The significant Yap1 down-regulation and also translocation of Yap1 into the cytoplasm during P19 differentiation was also detected. Moreover, our results suggest the E2f7, Lin28a and Dppa4 genes as possible regulatory nuclear factors of Hippo pathway in stem cells. The present findings are actively consistent with studies that suggested Yap1 as an essential factor for stem cell self-renewal.

Association Patterns of Ontological Features Signify Electronic Health Records in Liver Cancer

Electronic Health Record (EHR) system enables clinical decision support. In this study, a set of 112 abdominal computed tomography imaging examination reports, consisting of 59 cases of hepatocellular carcinoma (HCC) or liver metastases (so-called HCC group for simplicity) and 53 cases with no abnormality detected (NAD group), were collected from four hospitals in Hong Kong. We extracted terms related to liver cancer from the reports and mapped them to ontological features using Systematized Nomenclature of Medicine (SNOMED) Clinical Terms (CT). The primary predictor panel was formed by these ontological features. Association levels between every two features in the HCC and NAD groups were quantified using Pearson's correlation coefficient. The HCC group reveals a distinct association pattern that signifies liver cancer and provides clinical decision support for suspected cases, motivating the inclusion of new features to form the augmented predictor panel. Logistic regression analysis with stepwise forward procedure was applied to the primary and augmented predictor sets, respectively. The obtained model with the new features attained 84.7% sensitivity and 88.4% overall accuracy in distinguishing HCC from NAD cases, which were significantly improved when compared with that without the new features.

Co-expression Pattern Analysis of miR-17-92 Target Genes in Chronic Myelogenous Leukemia

MicroRNAs (miRNAs) are post-transcriptional regulators that regulate gene expression by binding to the 3' untranslated region of target mRNAs. Mature miRNAs transcribed from the miR-17-92 cluster have an oncogenic activity, which are overexpressed in chronic-phase chronic myelogenous leukemia (CML) patients compared with normal individuals. Besides, the tyrosine kinase activity of BCR-ABL oncoprotein from the Philadelphia chromosome in CML can affect this miRNA cluster. Genes with similar mRNA expression profiles are likely to be regulated by the same regulators. We hypothesize that target genes regulated by the same miRNA are co-expressed. In this study, we aim to explore the difference in the co-expression patterns of those genes potentially regulated by miR-17-92 cluster between the normal and the CML groups. We applied a statistical method for gene pair classification by identifying a disease-specific cutoff point that classified the co-expressed gene pairs into strong and weak co-expression classes. The method effectively identified the differences in the co-expression patterns from the overall structure. Functional annotation for co-expressed gene pairs showed that genes involved in the metabolism processes were more likely to be co-expressed in the normal group compared to the CML group. Our method can identify the co-expression pattern difference from the overall structure between two different distributions using the distribution-based statistical method. Functional annotation further provides the biological support. The co-expression pattern in the normal group is regarded as the inter-gene linkages, which represents the healthy pathological balance. Dysregulation of metabolism may be related to CML pathology. Our findings will provide useful information for investigating the novel CML mechanism and treatment.