A literature mining-based approach for identification of cellular pathways associated with chemoresistance in cancer

Microarray-based detection and expression analysis of drug resistance in an animal model of peritoneal metastasis from colon cancer

Chemotherapy drugs efficiently eradicate rapidly dividing differentiated cells by inducing cell death, but poorly target slowly dividing cells, including cancer stem cells and dormant cancer cells, in the later course of treatment. Prolonged exposure to chemotherapy results in a decrease in the proportion of apoptotic cells in the tumour mass. To investigate and characterize the molecular basis of this phenomenon, microarray-based expression analysis was performed to compare tHcred2-DEVD-EGFP-caspase 3-sensor transfected C-26 tumour cells that were harvested after engraftment into mice treated with or without 5-FU. Peritoneal metastasis was induced by intraperitoneal injection of C-26 cells, which were subsequently reisolated from omental metastatic tumours after the mice were sacrificed by the end of the 10th day after tumour injection. The purity of reisolated tHcred2-DEVD-EGFP-caspase 3-sensor-expressing C-26 cells was confirmed using FLIM, and total RNA was extracted for gene expression profiling. The validation of relative transcript levels was carried out via real-time semiquantitative RT‒PCR assays. Our results demonstrated that chemotherapy induced the differential expression of mediators of cancer cell dormancy and cell survival-related genes and downregulation of both intrinsic and extrinsic apoptotic signalling pathways. Despite the fact that some differentially expressed genes, such as BMP7 and Prss11, have not been thoroughly studied in the context of chemoresistance thus far, they might be potential candidates for future studies on overcoming drug resistance.

A Novel HOXA10-Associated 5-Gene-Based Prognostic Signature for Stratification of Short-term Survivors of Pancreatic Ductal Adenocarcinoma

PURPOSE

Despite the significant association of molecular subtypes with poor prognosis in patients with pancreatic ductal adenocarcinoma (PDAC), few efforts have been made to identify the underlying pathway(s) responsible for this prognosis. Identifying a clinically relevant prognosis-based gene signature may be the key to improving patient outcomes.

EXPERIMENTAL DESIGN

We analyzed the transcriptomic profiles of treatment-naïve surgically resected short-term survivor (STS) and long-term survivor (LTS) tumors (GSE62452) for expression and survival, followed by validation in several datasets. These results were corroborated by IHC analysis of PDAC-resected STS and LTS tumors. The mechanism of this differential survival was investigated using CIBERSORT and pathway analyses.

RESULTS

We identified a short-surviving prognostic subtype of PDAC with a high degree of significance (P = 0.018). One hundred thirty genes in this novel subtype were found to be regulated by a master regulator, homeobox gene HOXA10, and a 5-gene signature derived from these genes, including BANF1, EIF4G1, MRPS10, PDIA4, and TYMS, exhibited differential expression in STSs and a strong association with poor survival. This signature was further associated with the proportion of T cells and macrophages found in STSs and LTSs, demonstrating a potential role in PDAC immunosuppression. Pathway analyses corroborated these findings, revealing that this HOXA10-driven prognostic signature is associated with immune suppression and enhanced tumorigenesis.

CONCLUSIONS

Overall, these findings reveal the presence of a HOXA10-associated prognostic subtype that can be used to differentiate between STS and LTS patients of PDAC and inform on the molecular interactions that play a role in this poor prognosis.

Recent Advances and Implication of Bioengineered Nanomaterials in Cancer Theranostics

Cancer is one of the most common causes of death and affects millions of lives every year. In addition to non-infectious carcinogens, infectious agents contribute significantly to increased incidence of several cancers. Several therapeutic techniques have been used for the treatment of such cancers. Recently, nanotechnology has emerged to advance the diagnosis, imaging, and therapeutics of various cancer types. Nanomaterials have multiple advantages over other materials due to their small size and high surface area, which allow retention and controlled drug release to improve the anti-cancer property. Most cancer therapies have been known to damage healthy cells due to poor specificity, which can be avoided by using nanosized particles. Nanomaterials can be combined with various types of biomaterials to make it less toxic and improve its biocompatibility. Based on these properties, several nanomaterials have been developed which possess excellent anti-cancer efficacy potential and improved diagnosis. This review presents the latest update on novel nanomaterials used to improve the diagnostic and therapeutic of pathogen-associated and non-pathogenic cancers. We further highlighted mechanistic insights into their mode of action, improved features, and limitations.

Characterizing Cancer Drug Response and Biological Correlates: A Geometric Network Approach

In the present work, we apply a geometric network approach to study common biological features of anticancer drug response. We use for this purpose the panel of 60 human cell lines (NCI-60) provided by the National Cancer Institute. Our study suggests that mathematical tools for network-based analysis can provide novel insights into drug response and cancer biology. We adopted a discrete notion of Ricci curvature to measure, via a link between Ricci curvature and network robustness established by the theory of optimal mass transport, the robustness of biological networks constructed with a pre-treatment gene expression dataset and coupled the results with the GI50 response of the cell lines to the drugs. Based on the resulting drug response ranking, we assessed the impact of genes that are likely associated with individual drug response. For genes identified as important, we performed a gene ontology enrichment analysis using a curated bioinformatics database which resulted in biological processes associated with drug response across cell lines and tissue types which are plausible from the point of view of the biological literature. These results demonstrate the potential of using the mathematical network analysis in assessing drug response and in identifying relevant genomic biomarkers and biological processes for precision medicine.

SparkText: Biomedical Text Mining on Big Data Framework

Background

Many new biomedical research articles are published every day, accumulating rich information, such as genetic variants, genes, diseases, and treatments. Rapid yet accurate text mining on large-scale scientific literature can discover novel knowledge to better understand human diseases and to improve the quality of disease diagnosis, prevention, and treatment.

Results

In this study, we designed and developed an efficient text mining framework called SparkText on a Big Data infrastructure, which is composed of Apache Spark data streaming and machine learning methods, combined with a Cassandra NoSQL database. To demonstrate its performance for classifying cancer types, we extracted information (e.g., breast, prostate, and lung cancers) from tens of thousands of articles downloaded from PubMed, and then employed Naïve Bayes, Support Vector Machine (SVM), and Logistic Regression to build prediction models to mine the articles. The accuracy of predicting a cancer type by SVM using the 29,437 full-text articles was 93.81%. While competing text-mining tools took more than 11 hours, SparkText mined the dataset in approximately 6 minutes.

Conclusions

This study demonstrates the potential for mining large-scale scientific articles on a Big Data infrastructure, with real-time update from new articles published daily. SparkText can be extended to other areas of biomedical research.

Biological Networks for Cancer Candidate Biomarkers Discovery

Due to its extraordinary heterogeneity and complexity, cancer is often proposed as a model case of a systems biology disease or network disease. There is a critical need of effective biomarkers for cancer diagnosis and/or outcome prediction from system level analyses. Methods based on integrating omics data into networks have the potential to revolutionize the identification of cancer biomarkers. Deciphering the biological networks underlying cancer is undoubtedly important for understanding the molecular mechanisms of the disease and identifying effective biomarkers. In this review, the networks constructed for cancer biomarker discovery based on different omics level data are described and illustrated from recent advances in the field.