CancerDiscover: an integrative pipeline for cancer biomarker and cancer class prediction from high-throughput sequencing data

Blood miRNAs miR-549a, miR-552, and miR-592 serve as potential disease-specific panels to diagnose colorectal cancer

Introduction

miRNAs originating from colorectal cancer (CRC) tissue receive significant focus in the early diagnosis of CRC due to their stability in body fluids. However, if these miRNAs originated from alternative organs, their prognostic value will diminish. Thus, in this study, we aim to identify disease-specific miRNAs for colorectal cancer (CRC) by employing bioinformatics and experimental methodologies.

Method

To identify CRC-specific miRNAs, we retrieved miRNA profiles of CRC and normal tissues from the Cancer Genome Atlas (TCGA) database. Subsequently, computational strategies were utilized to select potential candidate miRNAs. Following this, the expression levels of the potent miRNAs were assessed through RT-qPCR in both CRC tissue and serum samples from patients (N = 46), as well as healthy individuals (N = 46). Additionally, the associations between clinicopathological characteristics, survival outcomes, and diagnostic accuracy were evaluated.

Results

A total of 8893 RNA-seq expression data were acquired from TCGA, comprising 8250 data from 19 distinct cancer types and 643 corresponding healthy samples. Based on the computational methodology, miR-549a, miR-552, and miR-592 were identified as the principal expressed miRNAs in colorectal cancer (CRC). Within these miRNAs, miR-552 displayed a substantial association with tumors at the N and T stages. miR-549a and miR-592 were observed to be linked exclusively to the invasion of tumor depth and tumor stage (TNM), respectively. The receiver operating characteristic (ROC) analysis conducted on the miRNA expression in serum samples revealed that all miRNAs exhibited an area under the ROC curve (AUC) of up to 0.86, thereby indicating their high diagnostic accuracy.

Conclusion

Considering the strong associations of these three identified miRNAs with CRC, they can collectively serve as a panel for specific discrimination of CRC from other types of cancer within the body. Although this study focused solely on CRC, this approach can potentially be applied to other cancer types as well.

A Review on Graphene Analytical Sensors for Biomarker-based Detection of Cancer

The engineering of nanoscale materials has broadened the scope of nanotechnology in a restricted functional system. Today, significant priority is given to immediate health diagnosis and monitoring tools for point-of-care testing and patient care. Graphene, as a one-atom carbon compound, has the potential to detect cancer biomarkers and its derivatives. The atom-wide graphene layer specialises in physicochemical characteristics, such as improved electrical and thermal conductivity, optical transparency, and increased chemical and mechanical strength, thus making it the best material for cancer biomarker detection. The outstanding mechanical, electrical, electrochemical, and optical properties of two-dimensional graphene can fulfil the scientific goal of any biosensor development, which is to develop a more compact and portable point-of-care device for quick and early cancer diagnosis. The bio-functionalisation of recognised biomarkers can be improved by oxygenated graphene layers and their composites. The significance of graphene that gleans its missing data for its high expertise to be evaluated, including the variety in surface modification and analytical reports. This review provides critical insights into graphene to inspire research that would address the current and remaining hurdles in cancer diagnosis.

Recent applications of quantitative systems pharmacology and machine learning models across diseases

Quantitative systems pharmacology (QSP) is a quantitative and mechanistic platform describing the phenotypic interaction between drugs, biological networks, and disease conditions to predict optimal therapeutic response. In this meta-analysis study, we review the utility of the QSP platform in drug development and therapeutic strategies based on recent publications (2019-2021). We gathered recent original QSP models and described the diversity of their applications based on therapeutic areas, methodologies, software platforms, and functionalities. The collection and investigation of these publications can assist in providing a repository of recent QSP studies to facilitate the discovery and further reusability of QSP models. Our review shows that the largest number of QSP efforts in recent years is in Immuno-Oncology. We also addressed the benefits of integrative approaches in this field by presenting the applications of Machine Learning methods for drug discovery and QSP models. Based on this meta-analysis, we discuss the advantages and limitations of QSP models and propose fields where the QSP approach constitutes a valuable interface for more investigations to tackle complex diseases and improve drug development.

A machine learning-based gene signature of response to the novel alkylating agent LP-184 distinguishes its potential tumor indications

BACKGROUND

Non-targeted cytotoxics with anticancer activity are often developed through preclinical stages using response criteria observed in cell lines and xenografts. A panel of the NCI-60 cell lines is frequently the first line to define tumor types that are optimally responsive. Open data on the gene expression of the NCI-60 cell lines, provides a unique opportunity to add another dimension to the preclinical development of such drugs by interrogating correlations with gene expression patterns. Machine learning can be used to reduce the complexity of whole genome gene expression patterns to derive manageable signatures of response. Application of machine learning in early phases of preclinical development is likely to allow a better positioning and ultimate clinical success of molecules. LP-184 is a highly potent novel alkylating agent where the preclinical development is being guided by a dedicated machine learning-derived response signature. We show the feasibility and the accuracy of such a signature of response by accurately predicting the response to LP-184 validated using wet lab derived IC50s on a panel of cell lines.

RESULTS

We applied our proprietary RADR® platform to an NCI-60 discovery dataset encompassing LP-184 IC50s and publicly available gene expression data. We used multiple feature selection layers followed by the XGBoost regression model and reduced the complexity of 20,000 gene expression values to generate a 16-gene signature leading to the identification of a set of predictive candidate biomarkers which form an LP-184 response gene signature. We further validated this signature and predicted response to an additional panel of cell lines. Considering fold change differences and correlation between actual and predicted LP-184 IC50 values as validation performance measures, we obtained 86% accuracy at four-fold cut-off, and a strong (r = 0.70) and significant (p value 1.36e-06) correlation between actual and predicted LP-184 sensitivity. In agreement with the perceived mechanism of action of LP-184, PTGR1 emerged as the top weighted gene.

CONCLUSION

Integration of a machine learning-derived signature of response with in vitro assessment of LP-184 efficacy facilitated the derivation of manageable yet robust biomarkers which can be used to predict drug sensitivity with high accuracy and clinical value.

The Translational Status of Cancer Liquid Biopsies

Abstract

Precision oncology aims to tailor clinical decisions specifically to patients with the objective of improving treatment outcomes. This can be achieved by leveraging omics information for accurate molecular characterization of tumors. Tumor tissue biopsies are currently the main source of information for molecular profiling. However, biopsies are invasive and limited in resolving spatiotemporal heterogeneity in tumor tissues. Alternative non-invasive liquid biopsies can exploit patient’s body fluids to access multiple layers of tumor-specific biological information (genomes, epigenomes, transcriptomes, proteomes, metabolomes, circulating tumor cells, and exosomes). Analysis and integration of these large and diverse datasets using statistical and machine learning approaches can yield important insights into tumor biology and lead to discovery of new diagnostic, predictive, and prognostic biomarkers. Translation of these new diagnostic tools into standard clinical practice could transform oncology, as demonstrated by a number of liquid biopsy assays already entering clinical use. In this review, we highlight successes and challenges facing the rapidly evolving field of cancer biomarker research.

Lay Summary

Precision oncology aims to tailor clinical decisions specifically to patients with the objective of improving treatment outcomes. The discovery of biomarkers for precision oncology has been accelerated by high-throughput experimental and computational methods, which can inform fine-grained characterization of tumors for clinical decision-making. Moreover, advances in the liquid biopsy field allow non-invasive sampling of patient’s body fluids with the aim of analyzing circulating biomarkers, obviating the need for invasive tumor tissue biopsies. In this review, we highlight successes and challenges facing the rapidly evolving field of liquid biopsy cancer biomarker research.

Differential gene expression analysis reveals novel genes and pathways in pediatric septic shock patients

Septic shock is a devastating health condition caused by uncontrolled sepsis. Advancements in high-throughput sequencing techniques have increased the number of potential genetic biomarkers under review. Multiple genetic markers and functional pathways play a part in development and progression of pediatric septic shock. We identified 53 differentially expressed pediatric septic shock biomarkers using gene expression data sampled from 181 patients admitted to the pediatric intensive care unit within the first 24 hours of their admission. The gene expression signatures showed discriminatory power between pediatric septic shock survivors and nonsurvivor types. Using functional enrichment analysis of differentially expressed genes, we validated the known genes and pathways in septic shock and identified the unexplored septic shock-related genes and functional groups. Differential gene expression analysis revealed the genes involved in the immune response, chemokine-mediated signaling, neutrophil chemotaxis, and chemokine activity and distinguished the septic shock survivor from non-survivor. The identification of the septic shock gene biomarkers may facilitate in septic shock diagnosis, treatment, and prognosis.