Proteogenomic studies on cancer drug resistance: towards biomarker discovery and target identification

Pharmacogenomics: Driving Personalized Medicine

Personalized medicine tailors therapies, disease prevention, and health maintenance to the individual, with pharmacogenomics serving as a key tool to improve outcomes and prevent adverse effects. Advances in genomics have transformed pharmacogenetics, traditionally focused on single gene-drug pairs, into pharmacogenomics, encompassing all "-omics" fields (e.g., proteomics, transcriptomics, metabolomics, and metagenomics). This review summarizes basic genomics principles relevant to translation into therapies, assessing pharmacogenomics' central role in converging diverse elements of personalized medicine. We discuss genetic variations in pharmacogenes (drug-metabolizing enzymes, drug transporters, and receptors), their clinical relevance as biomarkers, and the legacy of decades of research in pharmacogenetics. All types of therapies, including proteins, nucleic acids, viruses, cells, genes, and irradiation, can benefit from genomics, expanding the role of pharmacogenomics across medicine. Food and Drug Administration approvals of personalized therapeutics involving biomarkers increase rapidly, demonstrating the growing impact of pharmacogenomics. A beacon for all therapeutic approaches, molecularly targeted cancer therapies highlight trends in drug discovery and clinical applications. To account for human complexity, multicomponent biomarker panels encompassing genetic, personal, and environmental factors can guide diagnosis and therapies, increasingly involving artificial intelligence to cope with extreme data complexities. However, clinical application encounters substantial hurdles, such as unknown validity across ethnic groups, underlying bias in health care, and real-world validation. This review address the underlying science and technologies germane to pharmacogenomics and personalized medicine, integrated with economic, ethical, and regulatory issues, providing insights into the current status and future direction of health care. SIGNIFICANCE STATEMENT: Personalized medicine aims to optimize health care for the individual patients with use of predictive biomarkers to improve outcomes and prevent adverse effects. Pharmacogenomics drives biomarker discovery and guides the development of targeted therapeutics. This review addresses basic principles and current trends in pharmacogenomics, with large-scale data repositories accelerating medical advances. The impact of pharmacogenomics is discussed, along with hurdles impeding broad clinical implementation, in the context of clinical care, ethics, economics, and regulatory affairs.

Whole-exome sequencing reveals potential mechanisms of drug resistance to FGFR3-TACC3 targeted therapy and subsequent drug selection: towards a personalized medicine

Background

Drug resistance is a major obstacle to effective cancer therapy. In order to detect the change in tumor genomic states under drug selection pressure, we use next-generation sequencing technology to investigate the underlying potential mechanisms of drug resistance.

Methods

In our study, we presented a bladder cancer patient who had been a bona fide responder to first-line gemcitabine plus cisplatin regimen and second-line pazopanib (tyrosine kinase inhibitor (TKI) for FGFR3-TACC3 fusion) but finally had disease progression as an ideal case for showing genomic alteration during drug resistance. We applied whole-exome sequencing and ultra-deep target sequencing to the patient pre- and post- pazopanib resistance. Protein-protein interaction (PPI) network and Gene Ontology (GO) analyses were used to analysis protein interactions and genomic alterations. Patient-derived xenograft (PDX) model was built to test drug sensitivity.

Results

Twelve mutations scattered in 12 genes were identified by WES pre- pazopanib resistance, while 63 mutations in 50 genes arose post- pazopanib resistance. PPI network showed proteins from multiple epigenetic regulator families were involved post- pazopanib resistance, including subunits of chromatin remodeler SWI/SNF complex ARID1A/1B and SMARCA4, histone acetylation writers CREBBP, histone methylation writer NSD1 and erasers KDM6A/5A. GO enrichment analysis showed pazopanib resistance genes were prominently tagged for chromatin modification, transcription, as well as gland development, leaving genes with the best adaptive FGFR TKI-coping mechanisms. In addition, significantly elevated tumor mutational burden suggested possible utility of immunotherapy. Intriguingly, PDX model suggested that, sensitivity to original chemotherapy regimen (cisplatin) was restored in patient tumor post-pazopanib.

Conclusions

Epigenetic regulation may play a role in acquired TKI resistance. Our study traced the complete tumor genomic variation course from chemo-resistant but TKI-sensitive to TKI-resistant but chemo-(re) sensitive, revealing the potential complex dynamic drug-driven mechanisms of resistance.

Mechanism Analysis of Coix Seed in Gastric Cancer Treatment Based on Biological Network Modules

Coix seed, the mature seed of Coix lacryma-jobi L., is a traditional herb widely used in various cancer adjuvant treatments; however, its mechanism is unknown. The aim of this study was to reveal the multitarget mechanisms of Coix seed in the treatment of gastric cancer (GC) by biological network and modular analysis methods. Forty-one ingredients and 482 targets of Coix seed and 165 GC-related genes were obtained from databases. Twelve on-target genes ( AICDA, CASP3, EP300, ERBB2, FGFR2, IL12A, IL12B, IL1B, LOX, TJP1, TP53, and TRIB3) of Coix seed overlapped with GC-related genes. Using compound-target and protein–protein interaction network analyses, we discovered the core targets of Coix seed. Markov cluster algorithm-based modular analysis identified 5 potential module targets of Coix seed for GC. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis demonstrated the vast actions of Coix seed, which involve pathways in cancer, the cell cycle, receptor signal transduction, deoxyribonucleic acid damage response, transcriptional regulation, apoptosis, and cell connections. This study elucidated the potential mechanisms of Coix seed on GC, which may lead to the development of an effective drug. Additionally, this study showed the feasibility of network and modular analysis methods to investigate traditional Chinese medicinal herbal mechanisms and may provide a new angle for further research in the field of anticancer mechanisms and multitarget drugs.

A Review of ULK1-Mediated Autophagy in Drug Resistance of Cancer

The difficulty of early diagnosis and the development of drug resistance are two major barriers to the successful treatment of cancer. Autophagy plays a crucial role in several cellular functions, and its dysregulation is associated with both tumorigenesis and drug resistance. Unc-51-like kinase 1 (ULK1) is a serine/threonine kinase that participates in the initiation of autophagy. Many studies have indicated that compounds that directly or indirectly target ULK1 could be used for tumor therapy. However, reports of the therapeutic effects of these compounds have come to conflicting conclusions. In this work, we reviewed recent studies related to the effects of ULK1 on the regulation of autophagy and the development of drug resistance in cancers, with the aim of clarifying the mechanistic underpinnings of this therapeutic target.

Computational approaches in cancer multidrug resistance research: Identification of potential biomarkers, drug targets and drug-target interactions

Like physics in the 19th century, biology and molecular biology in particular, has been fertilized and enhanced like few other scientific fields, by the incorporation of mathematical methods. In the last decades, a whole new scientific field, bioinformatics, has developed with an output of over 30,000 papers a year (Pubmed search using the keyword "bioinformatics"). Huge databases of mass throughput data have been established, with ArrayExpress alone containing more than 2.7 million assays (October 2019). Computational methods have become indispensable tools in molecular biology, particularly in one of the most challenging areas of cancer research, multidrug resistance (MDR). However, confronted with a plethora of different algorithms, approaches, and methods, the average researcher faces key questions: Which methods do exist? Which methods can be used to tackle the aims of a given study? Or, more generally, how do I use computational biology/bioinformatics to bolster my research? The current review is aimed at providing guidance to existing methods with relevance to MDR research. In particular, we provide an overview on: a) the identification of potential biomarkers using expression data; b) the prediction of treatment response by machine learning methods; c) the employment of network approaches to identify gene/protein regulatory networks and potential key players; d) the identification of drug-target interactions; e) the use of bipartite networks to identify multidrug targets; f) the identification of cellular subpopulations with the MDR phenotype; and, finally, g) the use of molecular modeling methods to guide and enhance drug discovery. This review shall serve as a guide through some of the basic concepts useful in MDR research. It shall give the reader some ideas about the possibilities in MDR research by using computational tools, and, finally, it shall provide a short overview of relevant literature.

Molecular insights into cancer drug resistance from a proteomics perspective

INTRODUCTION

Resistance to chemotherapy and development of specific and effective molecular targeted therapies are major obstacles facing current cancer treatment. Comparative proteomic approaches have been employed for the discovery of putative biomarkers associated with cancer drug resistance and have yielded a number of candidate proteins, showing great promise for both novel drug target identification and personalized medicine for the treatment of drug-resistant cancer. Areas covered: Herein, we review the recent advances and challenges in proteomics studies on cancer drug resistance with an emphasis on biomarker discovery, as well as understanding the interconnectivity of proteins in disease-related signaling pathways. In addition, we highlight the critical role that post-translational modifications (PTMs) play in the mechanisms of cancer drug resistance. Expert opinion: Revealing changes in proteome profiles and the role of PTMs in drug-resistant cancer is key to deciphering the mechanisms of treatment resistance. With the development of sensitive and specific mass spectrometry (MS)-based proteomics and related technologies, it is now possible to investigate in depth potential biomarkers and the molecular mechanisms of cancer drug resistance, assisting the development of individualized therapeutic strategies for cancer patients.

Proteogenomics in the context of the Human Proteome Project (HPP)

INTRODUCTION

The technological and scientific progress performed in the Human Proteome Project (HPP) has provided to the scientific community a new set of experimental and bioinformatic methods in the challenging field of shotgun and SRM/MRM-based Proteomics. The requirements for a protein to be considered experimentally validated are now well-established, and the information about the human proteome is available in the neXtProt database, while targeted proteomic assays are stored in SRMAtlas. However, the study of the missing proteins continues being an outstanding issue. Areas covered: This review is focused on the implementation of proteogenomic methods designed to improve the detection and validation of the missing proteins. The evolution of the methodological strategies based on the combination of different omic technologies and the use of huge publicly available datasets is shown taking the Chromosome 16 Consortium as reference. Expert commentary: Proteogenomics and other strategies of data analysis implemented within the C-HPP initiative could be used as guidance to complete in a near future the catalog of the human proteins. Besides, in the next years, we will probably witness their use in the B/D-HPP initiative to go a step forward on the implications of the proteins in the human biology and disease.

Optimizing miRNA-module diagnostic biomarkers of gastric carcinoma via integrated network analysis

Several microRNAs (miRNAs) have been suggested as novel biomarkers for diagnosing gastric cancer (GC) at an early stage, but the single-marker strategy may ignore the co-regulatory relationships and lead to low diagnostic specificity. Thus, multi-target modular diagnostic biomarkers are urgently needed. In this study, a Z_summary and NetSVM-based method was used to identify GC-related hub miRNAs and activated modules from clinical miRNA co-expression networks. The NetSVM-based sub-network consisting of the top 20 hub miRNAs reached a high sensitivity and specificity of 0.94 and 0.82. The Z_summary algorithm identified an activated module (miR-486, miR-451, miR-185, and miR-600) which might serve as diagnostic biomarker of GC. Three members of this module were previously suggested as biomarkers of GC and its 24 target genes were significantly enriched in pathways directly related to cancer. The weighted diagnostic ROC AUC of this module was 0.838, and an optimized module unit (miR-451 and miR-185) obtained a higher value of 0.904, both of which were higher than that of individual miRNAs. These hub miRNAs and module have the potential to become robust biomarkers for early diagnosis of GC with further validations. Moreover, such modular analysis may offer valuable insights into multi-target approaches to cancer diagnosis and treatment.