Imputation of Gene Expression Data in Blood Cancer and Its Significance in Inferring Biological Pathways

Synergy between imputed genetic pathway and clinical information for predicting recurrence in early stage non-small cell lung cancer

OBJECTIVE

Lung cancer exhibits unpredictable recurrence in low-stage tumors and variable responses to different therapeutic interventions. Predicting relapse in early-stage lung cancer can facilitate precision medicine and improve patient survivability. While existing machine learning models rely on clinical data, incorporating genomic information could enhance their efficiency. This study aims to impute and integrate specific types of genomic data with clinical data to improve the accuracy of machine learning models for predicting relapse in early-stage, non-small cell lung cancer patients.

METHODS

The study utilized a publicly available TCGA lung cancer cohort and imputed genetic pathway scores into the Spanish Lung Cancer Group (SLCG) data, specifically in 1348 early-stage patients. Initially, tumor recurrence was predicted without imputed pathway scores. Subsequently, the SLCG data were augmented with pathway scores imputed from TCGA. The integrative approach aimed to enhance relapse risk prediction performance.

RESULTS

The integrative approach achieved improved relapse risk prediction with the following evaluation metrics: an area under the precision-recall curve (PR-AUC) score of 0.75, an area under the ROC (ROC-AUC) score of 0.80, an F1 score of 0.61, and a Precision of 0.80. The prediction explanation model SHAP (SHapley Additive exPlanations) was employed to explain the machine learning model's predictions.

CONCLUSION

We conclude that our explainable predictive model is a promising tool for oncologists that addresses an unmet clinical need of post-treatment patient stratification based on the relapse risk while also improving the predictive power by incorporating proxy genomic data not available for specific patients.

A comprehensive survey on computational learning methods for analysis of gene expression data

Computational analysis methods including machine learning have a significant impact in the fields of genomics and medicine. High-throughput gene expression analysis methods such as microarray technology and RNA sequencing produce enormous amounts of data. Traditionally, statistical methods are used for comparative analysis of gene expression data. However, more complex analysis for classification of sample observations, or discovery of feature genes requires sophisticated computational approaches. In this review, we compile various statistical and computational tools used in analysis of expression microarray data. Even though the methods are discussed in the context of expression microarrays, they can also be applied for the analysis of RNA sequencing and quantitative proteomics datasets. We discuss the types of missing values, and the methods and approaches usually employed in their imputation. We also discuss methods of data normalization, feature selection, and feature extraction. Lastly, methods of classification and class discovery along with their evaluation parameters are described in detail. We believe that this detailed review will help the users to select appropriate methods for preprocessing and analysis of their data based on the expected outcome.

Identification of key genes and miRNA-mRNA regulatory networks associated with bone marrow immune microenvironment regulations in multiple myeloma by integrative bioinformatics analysis

The deregulation of microRNAs (miRNAs) and genes in the bone marrow microenvironment have been involved with the pathogenesis of multiple myeloma (MM). However, the exploration of miRNA-mRNA regulatory networks in MM remains lacking. We used GSE125363, GSE125361, GSE47552, GSE2658, GSE136324, GSE16558, and GSE13591 datasets for this bioinformatics study. We identified 156 downregulated and 13 upregulated differentially expressed miRNAs (DEmiRs) in MM. The DEmiRs are associated with the enrichment of pathways mainly involved with cancers, cellular signaling, and immune regulations. We identified 112 hub genes associated with five significant clusters in MM. Moreover, we identified 9 upregulated hub genes (such as IGF1, RPS28, UBA52, CDKN1A, and CDKN2A) and 52 downregulated hub genes (such as TP53, PCNA, BRCA1, CCNB1, and MSH2) in MM that is targeted by DEmiRs. The expression of DEmiRs targeted two hub genes (CDKN2A and TP53) are correlated with the survival prognosis of MM patients. Furthermore, the expression level of CDKN2A is correlated with immune signatures, including CD4+ Regulatory T cells, T cell exhaustion, MHC Class I, immune checkpoint genes, macrophages, neutrophils, and TH2 cells in the TME of MM. Finally, we revealed the consistently deregulated expression level of key gene CDKN2A and its co-regulatory DEmiRs, including hsa-mir-192, hsa-mir-10b, hsa-mir-492, and hsa-mir-24 in the independent cohorts of MM. Identifying key genes and miRNA-mRNA regulatory networks may provide new molecular insights into the tumor immune microenvironment in MM.

Generalized SIR (GSIR) epidemic model: An improved framework for the predictive monitoring of COVID-19 pandemic

Novel coronavirus respiratory disease COVID-19 has caused havoc in many countries across the globe. In order to contain infection of this highly contagious disease, most of the world population is constrained to live in a complete or partial lockdown for months together with a minimal human-to-human interaction having far reaching consequences on countries' economy and mental well-being of their citizens. Hence, there is a need for a good predictive model for the health advisory bodies and decision makers for taking calculated proactive measures to contain the pandemic and maintain a healthy economy. This paper extends the mathematical theory of the classical Susceptible-Infected-Removed (SIR) epidemic model and proposes a Generalized SIR (GSIR) model that is an integrative model encompassing multiple waves of daily reported cases. Existing growth function models of epidemic have been shown as the special cases of the GSIR model. Dynamic modeling of the parameters reflect the impact of policy decisions, social awareness, and the availability of medication during the pandemic. GSIR framework can be utilized to find a good fit or predictive model for any pandemic. The study is performed on the COVID-19 data for various countries with detailed results for India, Brazil, United States of America (USA), and World. The peak infection, total expected number of COVID-19 cases and thereof deaths, time-varying reproduction number, and various other parameters are estimated from the available data using the proposed methodology. The proposed GSIR model advances the existing theory and yields promising results for continuous predictive monitoring of COVID-19 pandemic.

ARCANE-ROG: Algorithm for Reconstruction of Cancer Evolution from single-cell data using Robust Graph Learning

Tumor heterogeneity, marked by the presence of divergent clonal subpopulations of tumor cells, impedes the treatment response in cancer patients. Single-cell sequencing technology provides substantial prospects to gain an in-depth understanding of the cellular phenotypic variability driving tumor progression. A comprehensive insight into the intra-tumor heterogeneity may further assist in dealing with the treatment-resistant clones in cancer patients, thereby improving their overall survival. However, this task is hampered due to the challenges associated with the single-cell data, such as false positives, false negatives and missing bases, and the increase in their size. As a result, the computational cost of the existing methods increases, thereby limiting their usage. In this work, we propose a robust graph learning-based method, ARCANE-ROG (Algorithm for Reconstruction of CANcer Evolution via RObust Graph learning), for inferring clonal evolution from single-cell datasets. The first step of the proposed method is a joint framework of denoising with data imputation for the noisy and incomplete matrix while simultaneously learning an adjacency graph. Both the operations in the joint framework boost each other such that the overall performance of the denoising algorithm is improved. In the second step, an optimal number of clusters are identified via the Leiden method. In the last step, clonal evolution trees are inferred via a minimum spanning tree algorithm. The method has been benchmarked against a state-of-the-art method, RobustClone, using simulated datasets of varying sizes and five real datasets. The performance of our proposed method is found to be significantly superior (p-value < 0.05) in terms of reconstruction error, False Positive to False Negative (FPFN) ratio, tree distance error and V-measure compared to the other method. Overall, the proposed method is an improvement over the existing methods as it enhances cluster assignment and inference on clonal hierarchies.

Does Ethnicity Matter in Multiple Myeloma Risk Prediction in the Era of Genomics and Novel Agents? Evidence From Real-World Data

Introduction

Current risk predictors of multiple myeloma do not integrate ethnicity-specific information. However, the impact of ethnicity on disease biology cannot be overlooked. In this study, we have investigated the impact of ethnicity in multiple myeloma risk prediction. In addition, an efficient and robust artificial intelligence (AI)-enabled risk-stratification system is developed for newly diagnosed multiple myeloma (NDMM) patients that utilizes ethnicity-specific cutoffs of key prognostic parameters.

Methods

K-adaptive partitioning is used to propose new cutoffs of parameters for two different datasets—the MMIn (MM Indian dataset) dataset and the MMRF (Multiple Myeloma Research Foundation) dataset belonging to two different ethnicities. The Consensus-based Risk-Stratification System (CRSS) is designed using the Gaussian mixture model (GMM) and agglomerative clustering. CRSS is validated via Cox hazard proportional methods, Kaplan–Meier analysis, and log-rank tests on progression-free survival (PFS) and overall survival (OS). SHAP (SHapley Additive exPlanations) is utilized to establish the biological relevance of the risk prediction by CRSS.

Results

There is a significant variation in the key prognostic parameters of the two datasets belonging to two different ethnicities. CRSS demonstrates superior performance as compared with the R-ISS in terms of C-index and hazard ratios on both the MMIn and MMRF datasets. An online calculator has been built that can predict the risk stage of a multiple myeloma (MM) patient based on the values of parameters and ethnicity.

Conclusion

Our methodology discovers changes in the cutoffs with ethnicities from the established cutoffs of prognostic features. The best predictor model for both cohorts was obtained with the new ethnicity-specific cutoffs of clinical parameters. Our study also revealed the efficacy of AI in building a deployable risk prediction system for MM. In the future, it is suggested to use the CRSS risk calculator on a large dataset as the cohort size of the present study is 25% of the cohort used in the R-ISS reported in 2015.