Positive-Unlabeled Learning for inferring drug interactions based on heterogeneous attributes

BACKGROUND: Investigating and understanding drug-drug interactions (DDIs) is important in improving the effectiveness of clinical care. DDIs can occur when two or more drugs are administered together. Experimentally based DDI detection methods require a large cost and time. Hence, there is a great interest in developing efficient and useful computational methods for inferring potential DDIs. Standard binary classifiers require both positives and negatives for training. In a DDI context, drug pairs that are known to interact can serve as positives for predictive methods. But, the negatives or drug pairs that have been confirmed to have no interaction are scarce. To address this lack of negatives, we introduce a Positive-Unlabeled Learning method for inferring potential DDIs. RESULTS: The proposed method consists of three steps: i) application of Growing Self Organizing Maps to infer negatives from the unlabeled dataset; ii) using a pairwise similarity function to quantify the overlap between individual features of drugs and iii) using support vector machine classifier for inferring DDIs. We obtained 6036 DDIs from DrugBank database. Using the proposed approach, we inferred 589 drug pairs that are likely to not interact with each other; these drug pairs are used as representative data for the negative class in binary classification for DDI prediction. Moreover, we classify the predicted DDIs as Cytochrome P450 (CYP) enzyme-Dependent and CYP-Independent interactions invoking their locations on the Growing Self Organizing Map, due to the particular importance of these enzymes in clinically significant interaction effects. Further, we provide a case study on three predicted CYP-Dependent DDIs to evaluate the clinical relevance of this study. CONCLUSION: Our proposed approach showed an absolute improvement in F1-score of 14 and 38% in comparison to the method that randomly selects unlabeled data points as likely negatives, depending on the choice of similarity function. We inferred 5300 possible CYP-Dependent DDIs and 592 CYP-Independent DDIs with the highest posterior probabilities. Our discoveries can be used to improve clinical care as well as the research outcomes of drug development

Identification and Functional Characterization of PI3K/Akt/mTOR Pathway-Related lncRNAs in Lung Adenocarcinoma: A Retrospective Study

Objective: This paper aimed to investigate the PI3K/Akt/mTOR signal-pathway regulator factor-related lncRNAsignatures (PAM-SRFLncSigs), associated with regulators of the indicated signaling pathway in patients with lungadenocarcinoma (LUAD) undergoing immunotherapy.Materials and Methods: In this retrospective study, we employed univariate Cox, multivariate Cox, and least absoluteshrinkage and selection operator (LASSO) regression analyses to identify prognostically relevant long non-codingRNAs (lncRNAs), construct prognostic models, and perform Gene Ontology (GO) and Kyoto Encyclopedia of Genesand Genomes (KEGG) analyses. Subsequently, immunoassay and chemotherapy drug screening were conducted.Finally, the prognostic model was validated using the Imvigor210 cohort, and tumor stem cells were analyzed.Results: We identified seven prognosis-related lncRNAs (AC084757.3, AC010999.2, LINC02802, AC026979.2,AC024896.1, LINC00941 and LINC01312). We also developed prognostic models to predict survival in patientswith LUAD. KEGG enrichment analysis confirmed association of LUAD with the PI3K/Akt/mTOR signaling pathway.In the analysis of immune function pathways, we discovered three good prognostic pathways (Cytolytic_activity,Inflammation-promoting, T_cell_co-inhibition) in LUAD. Additionally, we screened 73 oncology chemotherapy drugsusing the "pRRophetic" algorithm.Conclusion: Identification of seven lncRNAs linked to regulators of the PI3K/Akt/mTOR signaling pathway providedvaluable insights into predicting the prognosis of LUAD, understanding the immune microenvironment and optimizingimmunotherapy strategies

An Evaluation of One Class Classifier on Gene Expression Data

It is not rare that medical data has imbalanced classes. This problem causes many difficulties when diagnosing rare diseases or cancer subtypes by machine learning tools, since traditional binary or multi-class classifiers lack the ability to classify imbalanced data. Therefore, One-Class Classifiers(OCC), the machine learning methods that only use data from one class,becomes one possible option. Our study evaluates ν-SVM, one of the most commonly used One-Class methods, on four microarray datasets of Breast Cancer and Diffuse large B-cell lymphoma (DLBCL). Each cancer is labelled into different subtypes. We compared OCC with binary SVM and studied how the imbalance between the classes affects the results. The results show that ν-SVM performs better than binary SVM when the data classes are extremely imbalanced on these datasets

Comparative Molecular Analysis of Gastrointestinal Adenocarcinomas

We analyzed 921 adenocarcinomas of the esophagus, stomach, colon, and rectum to examine shared and distinguishing molecular characteristics of gastrointestinal tract adenocarcinomas (GIACs). Hypermutated tumors were distinct regardless of cancer type and comprised those enriched for insertions/deletions, representing microsatellite instability cases with epigenetic silencing of MLH1 in the context of CpG island methylator phenotype, plus tumors with elevated single-nucleotide variants associated with mutations in POLE. Tumors with chromosomal instability were diverse, with gastroesophageal adenocarcinomas harboring fragmented genomes associated with genomic doubling and distinct mutational signatures. We identified a group of tumors in the colon and rectum lacking hypermutation and aneuploidy termed genome stable and enriched in DNA hypermethylation and mutations in KRAS, SOX9, and PCBP1. Liu et al. analyze 921 gastrointestinal (GI) tract adenocarcinomas and find that hypermutated tumors are enriched for insertions/deletions, upper GI tumors with chromosomal instability harbor fragmented genomes, and a group of genome-stable colorectal tumors are enriched in mutations in SOX9 and PCBP1

Proteogenomic landscape of breast cancer tumorigenesis and targeted therapy

The integration of mass spectrometry-based proteomics with next-generation DNA and RNA sequencing profiles tumors more comprehensively. Here this "proteogenomics" approach was applied to 122 treatment-naive primary breast cancers accrued to preserve post-translational modifications, including protein phosphorylation and acetylation. Proteogenomics challenged standard breast cancer diagnoses, provided detailed analysis of the ERBB2 amplicon, defined tumor subsets that could benefit from immune checkpoint therapy, and allowed more accurate assessment of Rb status for prediction of CDK4/6 inhibitor responsiveness. Phosphoproteomics profiles uncovered novel associations between tumor suppressor loss and targetable kinases. Acetylproteome analysis highlighted acetylation on key nuclear proteins involved in the DNA damage response and revealed cross-talk between cytoplasmic and mitochondrial acetylation and metabolism. Our results underscore the potential of proteogenomics for clinical investigation of breast cancer through more accurate annotation of targetable pathways and biological features of this remarkably heterogeneous malignancy

Constitutive PD-L1 expression in melanoma

Treatment of melanoma based on targeted therapy and immunotherapy has dramatically advanced over the past decade. Advances in targeted therapy have been based on inhibition of the MAPK pathway while for immunotherapy, advances have been based on blocking immune checkpoint proteins such as the PD-1/PD-L1 axis. PD-L1 serves as a potent immune suppressor of the immune response enabling cancer cells to escape the immune surveillance. Recently, it was reported in several studies that resistance to MAPK pathway inhibitors can be accompanied by increases in constitutive PD-L1 expression in melanoma, highlighting the importance of understanding the underlying regulation of PD-L1 expression. However, the mechanism regulating constitutive PD-L1 expression remains unclear in melanoma. In this study, one of the aims was to investigate whether DNA methylation plays a role in PD-L1 expression. Firstly, it was found that melanoma cell lines with constitutive PD-L1 expression have a marked loss of global DNA methylation (hypomethylation), particularly in the intergenic regions and repeat elements, which suggested an altered epigenomic landscape. A number of endogenous retrovirus (ERV) elements that reside in the intergenic region were increased in expression in the constitutive PD-L1 cell lines. This was accompanied by activation of the innate immune response and transcription factors that can upregulate PD-L1 levels. Intergenic lncRNAs that are in close proximity to immune related genes were also upregulated in the constitutive PD-L1 cell lines. Moreover, DNMTi (global demethylation) mediated PD-L1 upregulation was revealed to increase many of the same innate immune response genes and transcription factors that were upregulated in the constitutive PD-L1 samples supporting the role of DNA hypomethylation in PD-L1 expression. Furthermore, how PD-L1 expression is associated with resistance to MAPK targeted inhibitors remains unclear. Here, it was found that constitutive PD-L1 expression is associated with a transcriptomic state that is characteristic for dedifferentiation which is mediated by the loss of SOX10 expression and upregulation of other transcription factors such as SOX9. Moreover, constitutive PD-L1 samples were associated with a reduced expression of genes involved in oxidative phosphorylation demonstrating an altered metabolic program. Overall, we found evidence that supports constitutive PD-L1 expression in melanoma is regulated by the viral mimicry pathway via global hypomethylation. Furthermore, constitutive PD-L1 expression is closely associated with dedifferentiation mediated by loss of SOX10 which provides insight as to why PD-L1 expression increases upon development of therapy resistance

ONE-CLASS DETECTION OF CELL STATES IN TUMOR SUBTYPES

The cellular composition of a tumor greatly influences the growth, spread, immune activity, drug response, and other aspects of the disease. Tumor cells are usually comprised of a heterogeneous mixture of subclones, each of which could contain their own distinct character. The presence of minor subclones poses a serious health risk for patients as any one of them could harbor a fitness advantage with respect to the current treatment regimen, fueling resistance. It is therefore vital to accurately assess the make-up of cell states within a tumor biopsy. Transcriptome-wide assays from RNA sequencing provide key data from which cell state signatures can be detected. However, the challenge is to find them within samples containing mixtures of cell types of unknown proportions. We propose a novel one-class method based on logistic regression and show that its performance is competitive to two established SVM-based methods for this detection task. We demonstrate that one-class models are able to identify specific cell types in heterogeneous cell populations better than their binary predictor counterparts. We derive one-class predictors for the major breast and bladder subtypes and reaffirm the connection between these two tissues. In addition, we use a one-class predictor to quantitatively associate an embryonic stem cell signature with an aggressive breast cancer subtype that reveals shared stemness pathways potentially important for treatment