Understanding next generation sequencing in oncology: A guide for oncologists

Practical considerations for pathological diagnosis and molecular profiling of cholangiocarcinoma: an expert review for best practices

INTRODUCTION

Advances in precision medicine have expanded access to targeted therapies and demand for molecular profiling of cholangiocarcinoma (CCA) patients in routine clinical practice. However, pathologists face challenges in establishing a definitive intrahepatic CCA (iCCA) diagnosis while preserving sufficient tissue for molecular profiling. Additionally, they frequently face challenges in optimal tissue handling to preserve nucleic acid integrity.

AREAS COVERED

This article first identifies the challenges in establishing a definitive diagnosis of iCCA in a lesional liver biopsy while preserving sufficient tissue for molecular profiling. Then, the authors explore the clinical value of molecular profiling, the basic principles of single gene and next-generation sequencing (NGS) techniques, and the challenges in tissue sampling for genomic testing. They also propose an algorithm for best practice in tissue management for molecular profiling of CCA.

EXPERT OPINION

Several practical challenges face pathologists during tissue sampling and processing for molecular profiling. Optimized tissue processing, careful tissue handling, and selection of appropriate approaches to molecular testing are essential to ensure that the highest possible quality of diagnostic information is provided in the greatest proportion of cases.

[Use of genome-wide testing in oncology: French expert opinion based on the Delphi methodology]

In oncology, genome-wide testing is a major element in facilitating the implementation of precision medicine. However, current recommendations do not always specify the indication and utility of these tests according to the type of cancer. A national consensus approach based on a modified Delphi methodology was set up to provide expert opinion on the use of genome-wide testing in clinical practice in France. Four groups of experts - 4 each representing the following topics of interest - were defined: non-small cell lung cancer (NSCLC), breast cancer, melanoma, and cancer of unknown primary (CUP). In each group, assertions were formulated by a lead expert (8, 5, 7 and 6, respectively) and rated by five panellists involved in the management of these cancers, on a scale from 1 (strongly disagree) to 9 (strongly agree). Consensus was reached when 75% of the scores were above 7. In case of disagreement, the panellists were asked to justify their rate. In total, 24 statements reached consensus after two to four rounds of rating, depending on the group. While the experts advocated the routine use of genome-wide testing in the diagnostic management of NSCLC and CUP, they did not recommend the systematisation of these tests for breast cancer and melanoma. Nevertheless, access to innovation in France could soon remove certain barriers and allow greater standardisation of broad molecular screening in oncology.

Cerebrospinal Fluid biomarkers in pediatric brain tumors: A systematic review

Central nervous system (CNS) tumors are the leading cause of cancer death in pediatric patients. Though these tumors typically require invasive surgical procedures to diagnose, cerebrospinal fluid (CSF) liquid biopsy presents a potential method for rapid and noninvasive detection of markers of CNS malignancy. To characterize molecular biomarkers that can be used in the diagnosis, prognosis, and monitoring of pediatric cancer patients, a literature review was conducted in accordance with PRISMA guidelines. PubMed and EMBASE were searched for the terms biomarkers, liquid biopsy, cerebrospinal fluid, pediatric central nervous system tumor, and their synonyms. Studies including pediatric patients with CSF sampling for tumor evaluation were included. Studies were excluded if they did not have full text or if they were case studies, methodology reports, in languages other than English, or animal studies. Our search revealed 163 articles of which 42 were included. Proteomic, genomic, and small molecule markers associated with CNS tumors were identified for further analysis and development of detection tools.

Whole-Transcriptome Profiling on Small FFPE Samples: Which Sequencing Kit Should Be Used?

RNA sequencing (RNA-Seq) appears as a great tool with huge clinical potential, particularly in oncology. However, sufficient sample size is often a limiting factor and the vast majority of samples from patients with cancer are formalin-fixed paraffin-embedded (FFPE). To date, several sequencing kits are proposed for FFPE samples yet no comparison on low quantities were performed. To select the most reliable, cost-effective, and relevant RNA-Seq approach, we applied five FFPE-compatible kits (based on 3′ capture, exome-capture and ribodepletion approaches) using 8 ng to 400 ng of FFPE-derived RNA and compared them to Nanostring on FFPE samples and to a reference PolyA (Truseq) approach on flash-frozen samples of the same tumors. We compared gene expression correlations and reproducibility. The Smarter Pico V3 ribodepletion approach appeared systematically the most comparable to Nanostring and Truseq (p < 0.001) and was a highly reproducible technique. In comparison with exome-capture and 3′ kits, the Smarter appeared more comparable to Truseq (p < 0.001). Overall, our results suggest that the Smarter is the most robust RNA-Seq technique to study small FFPE samples and 3′ Lexogen presents an interesting quality−price ratio for samples with less limiting quantities.

FFPE-Based NGS Approaches into Clinical Practice: The Limits of Glory from a Pathologist Viewpoint

The introduction of next-generation sequencing (NGS) in the molecular diagnostic armamentarium is deeply changing pathology practice and laboratory frameworks. NGS allows for the comprehensive molecular characterization of neoplasms, in order to provide the best treatment to oncologic patients. On the other hand, NGS raises technical issues and poses several challenges in terms of education, infrastructures and costs. The aim of this review is to give an overview of the main NGS sequencing platforms that can be used in current molecular diagnostics and gain insights into the clinical applications of NGS in precision oncology. Hence, we also focus on the preanalytical, analytical and interpretative issues raised by the incorporation of NGS in routine pathology diagnostics.

Current status of precision medicine in pediatric oncology in Spain: a consensus report by the Spanish Society of Paediatric Haematology and Oncology (SEHOP)

The study analyzes the current status of personalized medicine in pediatric oncology in Spain. It gathers national data on the tumor molecular studies and genomic sequencing carried out at diagnosis and at relapse, the centers that perform these studies, the technology used and the interpretation and clinical applicability of the results. Current challenges and future directions to achieve a coordinated national personalized medicine strategy in pediatric oncology are also discussed. Next generation sequencing-based (NGS) gene panels are the technology used in the majority of centers and financial limitations are the main reason for not incorporating these studies into routine care. Nowadays, the application of precision medicine in pediatric oncology is a reality in a great number of Spanish centers. However, its implementation is uneven and lacks standardization of protocols; therefore, national coordination to overcome the inequalities is required. Collaborative work within the Personalized Medicine Group of SEHOP is an adequate framework for encouraging a step forward in the effort to move precision medicine into the national healthcare system.

Liquid Profiling for Cancer Patient Stratification in Precision Medicine—Current Status and Challenges for Successful Implementation in Standard Care

Circulating tumor DNA (ctDNA), accurately described by the term liquid profiling (LP), enables real-time assessment of the tumor mutational profile as a minimally invasive test and has therefore rapidly gained traction, particular for the management of cancer patients. By LP, tumor-specific genetic alterations can be determined as part of companion diagnostics to guide selection of appropriate targeted therapeutics. Because LP facilitates longitudinal monitoring of cancer patients, it can be used to detect acquired resistant mechanisms or as a personalized biomarker for earlier detection of disease recurrence, among other applications. However, LP is not yet integrated into routine care to the extent that might be expected. This is due to the lack of harmonization and standardization of preanalytical and analytical workflows, the lack of proper quality controls, limited evidence of its clinical utility, heterogeneous study results, the uncertainty of clinicians regarding the value and appropriate indications for LP and its interpretation, and finally, the lack of reimbursement for most LP tests. In this review, the value proposition of LP for cancer patient management and treatment optimization, the current status of implementation in standard care, and the main challenges that need to be overcome are discussed in detail.

Multigene Panel Sequencing Reveals Cancer-Specific and Common Somatic Mutations in Colorectal Cancer Patients: An Egyptian Experience

This study aims at identifying common pathogenic somatic mutations at different stages of colorectal carcinogenesis in Egyptian patients. Our cohort included colonoscopic biopsies collected from 120 patients: 20 biopsies from patients with inflammatory bowel disease, 38 from colonic polyp patients, and 62 from patients with colorectal cancer. On top of this, the cohort included 20 biopsies from patients with non-specific mild to moderated colitis. Targeted DNA sequencing using a customized gene panel of 96 colorectal related genes running on the Ion Torrent NGS technology was used to process the samples. Our results revealed that 69% of all cases harbored at least one somatic mutation. Fifty-seven genes were found to carry 232 somatic non-synonymous variants. The most frequently pathogenic somatic mutations were localized in TP53, APC, KRAS, and PIK3CA. In total, 16 somatic mutations were detected in the CRC group and in either the IBD or CP group. In addition, our data showed that 51% of total somatic variants were CRC-specific variants. The average number of CRC-specific variants per sample is 2.4. The top genes carrying CRC-specific mutations are APC, TP53, PIK3CA, FBXW7, ATM, and SMAD4. It seems obvious that TP53 and APC genes were the most affected genes with somatic mutations in all groups. Of interest, 85% and 28% of the APC and TP53 deleterious somatic mutations were located in Exon 14 and Exon 3, respectively. Besides, 37% and 28% of the total somatic mutations identified in APC and TP53 were CRC-specific variants, respectively. Moreover, we identified that, in 29 somatic mutations in 21 genes, their association with CRC patients was unprecedented. Ten detected variants were likely to be novel: six in PIK3CA and four variants in FBXW7. The detected P53, Wnt/βcatenin, Angiogenesis, EGFR, TGF-β and Interleukin signaling pathways were the most altered pathways in 22%, 16%, 12%, 10%, 9% and 9% of the CRC patients, respectively. These results would contribute to a better understanding of the colorectal cancer and in introducing personalized therapies for Egyptian CRC patients.

A Practical Guide to Integrating Multimodal Machine Learning and Metabolic Modeling

Complex, distributed, and dynamic sets of clinical biomedical data are collectively referred to as multimodal clinical data. In order to accommodate the volume and heterogeneity of such diverse data types and aid in their interpretation when they are combined with a multi-scale predictive model, machine learning is a useful tool that can be wielded to deconstruct biological complexity and extract relevant outputs. Additionally, genome-scale metabolic models (GSMMs) are one of the main frameworks striving to bridge the gap between genotype and phenotype by incorporating prior biological knowledge into mechanistic models. Consequently, the utilization of GSMMs as a foundation for the integration of multi-omic data originating from different domains is a valuable pursuit towards refining predictions. In this chapter, we show how cancer multi-omic data can be analyzed via multimodal machine learning and metabolic modeling. Firstly, we focus on the merits of adopting an integrative systems biology led approach to biomedical data mining. Following this, we propose how constraint-based metabolic models can provide a stable yet adaptable foundation for the integration of multimodal data with machine learning. Finally, we provide a step-by-step tutorial for the combination of machine learning and GSMMs, which includes: (i) tissue-specific constraint-based modeling; (ii) survival analysis using time-to-event prediction for cancer; and (iii) classification and regression approaches for multimodal machine learning. The code associated with the tutorial can be found at https://github.com/Angione-Lab/Tutorials_Combining_ML_and_GSMM .

A gentle introduction to understanding preclinical data for cancer pharmaco-omic modeling

A central goal of precision oncology is to administer an optimal drug treatment to each cancer patient. A common preclinical approach to tackle this problem has been to characterize the tumors of patients at the molecular and drug response levels, and employ the resulting datasets for predictive in silico modeling (mostly using machine learning). Understanding how and why the different variants of these datasets are generated is an important component of this process. This review focuses on providing such introduction aimed at scientists with little previous exposure to this research area.

Large-Scale Transcriptomics-Driven Approach Revealed Overexpression of CRNDE as a Poor Survival Prognosis Biomarker in Glioblastoma

Glioblastoma is the most common and malignant brain malignancy worldwide, with a 10-year survival of only 0.7%. Aggressive multimodal treatment is not enough to increase life expectancy and provide good quality of life for glioblastoma patients. In addition, despite decades of research, there are no established biomarkers for early disease diagnosis and monitoring of patient response to treatment. High throughput sequencing technologies allow for the identification of unique molecules from large clinically annotated datasets. Thus, the aim of our study was to identify significant molecular changes between short- and long-term glioblastoma survivors by transcriptome RNA sequencing profiling, followed by differential pathway-activation-level analysis. We used data from the publicly available repositories The Cancer Genome Atlas (TCGA; number of annotated cases = 135) and Chinese Glioma Genome Atlas (CGGA; number of annotated cases = 218), and experimental clinically annotated glioblastoma tissue samples from the Institute of Pathology, Faculty of Medicine in Ljubljana corresponding to 2-58 months overall survival (n = 16). We found one differential gene for long noncoding RNA CRNDE whose overexpression showed correlation to poor patient OS. Moreover, we identified overlapping sets of congruently regulated differential genes involved in cell growth, division, and migration, structure and dynamics of extracellular matrix, DNA methylation, and regulation through noncoding RNAs. Gene ontology analysis can provide additional information about the function of protein- and nonprotein-coding genes of interest and the processes in which they are involved. In the future, this can shape the design of more targeted therapeutic approaches.

Prospects Of Non-Coding Elements In Genomic Dna Based Gene Therapy

Gene therapy has made significant development since the commencement of the first clinical trials a few decades ago and has remained a dynamic area of research regardless of obstacles such as immune response and insertional mutagenesis. Progression in various technologies like next-generation sequencing (NGS) and nanotechnology has established the importance of non-coding segments of a genome, thereby taking gene therapy to the next level. In this review, we have summarized the importance of non-coding elements, highlighting the advantages of using full-length genomic DNA loci (gDNA) compared to complementary DNA (cDNA) or minigene, currently used in gene therapy. The focus of this review is to provide an overview of the advances and the future of potential use of gDNA loci in gene therapy, expanding the therapeutic repertoire in molecular medicine.

The Role of the Pathologist in the Next-Generation Era of Tumor Molecular Characterization

Current pathology practice is being shaped by the increasing complexity of modern medicine, in particular of precision oncology, and major technological advances. In the "next-generation technologies era", the pathologist has become the person responsible for the integration and interpretation of morphologic and molecular information and for the delivery of critical answers to diagnostic, prognostic and predictive queries, acquiring a prominent position in the molecular tumor boards.

Technical Evaluation of Commercial Mutation Analysis Platforms and Reference Materials for Liquid Biopsy Profiling

Molecular profiling from liquid biopsy, in particular cell-free DNA (cfDNA), represents an attractive alternative to tissue biopsies for the detection of actionable targets and tumor monitoring. In addition to PCR-based assays, Next Generation Sequencing (NGS)-based cfDNA assays are now commercially available and are being increasingly adopted in clinical practice. However, the validity of these products as well as the clinical utility of cfDNA in the management of patients with cancer has yet to be proven. Within framework of the Innovative Medicines Initiative (IMI) program CANCER-ID we evaluated the use of commercially available reference materials designed for ctDNA testing and cfDNA derived from Diagnostic Leukaphereses (DLA) for inter- and intra-assay as well as intra- and inter-laboratory comparisons. In three experimental setups, a broad range of assays including ddPCR, MassARRAY and various NGS-based assays were tested. We demonstrate that both reference materials with predetermined VAFs and DLA samples are extremely useful for the performance assessment of mutation analysis platforms. Moreover, our data indicate a substantial variability of NGS assays with respect to sensitivity and specificity highlighting the importance of extensive validation of the test performance before offering these tests in clinical routine practice.

Next Generation Sequencing and Machine Learning Technologies Are Painting the Epigenetic Portrait of Glioblastoma

Even with a rare occurrence of only 1.35% of cancer cases in the United States of America, brain tumors are considered as one of the most lethal malignancies. The most aggressive and invasive type of brain tumor, glioblastoma, accounts for 60–70% of all gliomas and presents with life expectancy of only 12–18 months. Despite trimodal treatment and advances in diagnostic and therapeutic methods, there are no significant changes in patient outcome. Our understanding of glioblastoma was significantly improved with the introduction of next generation sequencing technologies. This led to the identification of different genetic and molecular subtypes, which greatly improve glioblastoma diagnosis. Still, because of the poor life expectancy, novel diagnostic, and treatment methods are broadly explored. Epigenetic modifications like methylation and changes in histone acetylation are such examples. Recently, in addition to genetic and molecular characteristics, epigenetic profiling of glioblastomas is also used for sample classification. Further advancement of next generation sequencing technologies is expected to identify in detail the epigenetic signature of glioblastoma that can open up new therapeutic opportunities for glioblastoma patients. This should be complemented with the use of computational power i.e., machine and deep learning algorithms for objective diagnostics and design of individualized therapies. Using a combination of phenotypic, genotypic, and epigenetic parameters in glioblastoma diagnostics will bring us closer to precision medicine where therapies will be tailored to suit the genetic profile and epigenetic signature of the tumor, which will grant longer life expectancy and better quality of life. Still, a number of obstacles including potential bias, availability of data for minorities in heterogeneous populations, data protection, and validation and independent testing of the learning algorithms have to be overcome on the way.

Molecular profiling for precision cancer therapies

The number of druggable tumor-specific molecular aberrations has grown substantially in the past decade, with a significant survival benefit obtained from biomarker matching therapies in several cancer types. Molecular pathology has therefore become fundamental not only to inform on tumor diagnosis and prognosis but also to drive therapeutic decisions in daily practice. The introduction of next-generation sequencing technologies and the rising number of large-scale tumor molecular profiling programs across institutions worldwide have revolutionized the field of precision oncology. As comprehensive genomic analyses become increasingly available in both clinical and research settings, healthcare professionals are faced with the complex tasks of result interpretation and translation. This review summarizes the current and upcoming approaches to implement precision cancer medicine, highlighting the challenges and potential solutions to facilitate the interpretation and to maximize the clinical utility of molecular profiling results. We describe novel molecular characterization strategies beyond tumor DNA sequencing, such as transcriptomics, immunophenotyping, epigenetic profiling, and single-cell analyses. We also review current and potential applications of liquid biopsies to evaluate blood-based biomarkers, such as circulating tumor cells and circulating nucleic acids. Last, lessons learned from the existing limitations of genotype-derived therapies provide insights into ways to expand precision medicine beyond genomics.

Pharmacogenomic Testing: Clinical Evidence and Implementation Challenges

Pharmacogenomics can enhance patient care by enabling treatments tailored to genetic make-up and lowering risk of serious adverse events. As of June 2019, there are 132 pharmacogenomic dosing guidelines for 99 drugs and pharmacogenomic information is included in 309 medication labels. Recently, the technology for identifying individual-specific genetic variants (genotyping) has become more accessible. Next generation sequencing (NGS) is a cost-effective option for genotyping patients at many pharmacogenomic loci simultaneously, and guidelines for implementation of these data are available from organizations such as the Clinical Pharmacogenetics Implementation Consortium (CPIC) and the Dutch Pharmacogenetics Working Group (DPWG). NGS and related technologies are increasing knowledge in the research sphere, yet rates of genomic literacy remain low, resulting in a widening gap in knowledge translation to the patient. Multidisciplinary teams—including physicians, nurses, genetic counsellors, and pharmacists—will need to combine their expertise to deliver optimal pharmacogenomically-informed care.

Differentially expressed lncRNAs and mRNAs identified by NGS analysis in colorectal cancer patients

Long noncoding RNAs (lncRNAs) play an important role in gene regulation, but their impact on the pathogenesis of colorectal cancer and the biological function of cancer cells is unclear. In this study, we used next‐generation sequencing to study the differences in the expression profiles of lncRNAs and mRNAs in colorectal cancer tissues. We analyzed the differentially expressed genes by Gene Ontology/Kyoto Encyclopedia of Genes and Genomes (GO/KEGG) enrichment and predicted new lncRNA functions. Our results revealed that compared with lncRNAs and mRNAs in nontumor colorectal tissues, 1019 lncRNAs (512 upregulated, 507 downregulated) and 3221 mRNAs (1606 upregulated, 1615 downregulated) were differentially expressed in tumor colorectal tissues (fold change >2 and P < 0.05). We validated some of these genes by qPCR. Furthermore, we identified some new lncRNAs differently expressed in colorectal cancer samples from patients in northern China. We confirmed the function of lncRNA‐FIRRE‐201 and SLCO4A1‐AS1‐202 in colorectal cancer cells to provide an experimental basis for studies on their roles in the occurrence and development of colorectal cancer and in the regulation of networks.

Sequencing the next generation of glioblastomas

The most aggressive brain malignancy, glioblastoma, accounts for 60-70% of all gliomas and is uniformly fatal. According to the molecular signature, glioblastoma is divided into four subtypes (proneural, neural, classical, and mesenchymal), each with its own genetic background. The Cancer Genome Atlas project provides information about the most common genetic changes in glioblastoma. They involve mutations in TP53, TERT, and PTEN, and amplifications in EFGR, PDGFRA, CDK4, CDK6, MDM2, and MDM4. Recently, epigenetics was used to demonstrate the oncogenic roles of miR-124, miR-137, and miR-128. The most important findings so far are mutations in IDH1/2 and MGMT promoter methylation, which are routinely used as predictive biomarkers in patient care. Current clinical treatment leaves patients with only a 10% chance for 5-year survival. Attempts to define the mutational profile of glioblastoma to identify clinically relevant changes have not yet yielded significant results. This can be attributed to inter- and intra-tumor heterogeneity that is present in most glioblastomas, as well as hypermutation that appears as a consequence of chemotherapy. The evolving field of radiogenomics aims to classify glioblastoma using a combination of magnetic resonance imaging and genomic information. In the era of genomic medicine, next-generation sequencing is extensively used in glioblastoma research because it can detect multiple changes in a single biological sample; its potential in detecting circulating cell-free DNA has been tested in cerebrospinal fluid and plasma, and it shows promise in the examination of the cellular content of extracellular vesicles as a potential source of biomarkers. Next-generation sequencing is making its way into glioblastoma diagnostics. Gene panels like GlioSeq, which includes the most commonly mutated genes, are currently being tested on snap frozen and formalin fixed paraffin embedded tissues. This new methodology is helping to define the "next generation of glioblastomas" - clinically defined and better understood, with greater potential to improve patient care. However, limitations of the necessary infrastructure, space for data storage, technical expertise, and data ownership need to be considered carefully.

Novel putative drivers revealed by targeted exome sequencing of advanced solid tumors

Next generation sequencing (NGS) is becoming increasingly integrated into oncological practice and clinical research. NGS methods have also provided evidence for clonal evolution of cancers during disease progression and treatment. The number of variants associated with response to specific therapeutic agents keeps increasing. However, the identification of novel driver mutations as opposed to passenger (phenotypically silent or clinically irrelevant) mutations remains a major challenge. We conducted targeted exome sequencing of advanced solid tumors from 44 pre-treated patients with solid tumors including breast, colorectal and lung carcinomas, neuroendocrine tumors, sarcomas and others. We catalogued established driver mutations and putative new drivers as predicted by two distinct algorithms. The established drivers we detected were consistent with published observations. However, we also detected a significant number of mutations with driver potential never described before in each tumor type we studied. These putative drivers belong to key cell fate regulatory networks, including potentially druggable pathways. Should our observations be confirmed, they would support the hypothesis that new driver mutations are selected by treatment in clinically aggressive tumors, and indicate a need for longitudinal genomic testing of solid tumors to inform second line cancer treatment.

Novel applications of next-generation sequencing in breast cancer research

With the rapid development of medicine, the studies of genes have become increasingly concerned by more people and being the contend of a great of researches. The next generation sequencing with its own advantages has been widely used in gene research nowadays. It has almost replaced the traditional sequencing methods (such as Sanger sequencing method), and played an important role in a variety of complex disease researches, including breast cancer. The next generation sequencing technology has the advantages of high speed, high throughput and high accuracy. It has been widely used in various cancers (such as prostate cancer, lung cancer, pancreatic cancer, liver cancer, etc.), especially in breast cancer. Moreover, the use of the next generation sequencing technology to make DNA sequence analysis and risk prediction has made a great contribution to the research of breast cancer. We will focus on the application of whole genome sequencing, exon sequencing and targeted gene sequencing in breast cancer gene research.

Clinical Application of Targeted Next Generation Sequencing for Colorectal Cancers

Promising targeted therapy and personalized medicine are making molecular profiling of tumours a priority. For colorectal cancer (CRC) patients, international guidelines made RAS (KRAS and NRAS) status a prerequisite for the use of anti-epidermal growth factor receptor agents (anti-EGFR). Daily, new data emerge on the theranostic and prognostic role of molecular biomarkers, which is a strong incentive for a validated, sensitive and broadly available molecular screening test in order to implement and improve multi-modal therapy strategy and clinical trials. Next generation sequencing (NGS) has begun to supplant other technologies for genomic profiling. Targeted NGS is a method that allows parallel sequencing of thousands of short DNA sequences in a single test offering a cost-effective approach for detecting multiple genetic alterations with a minimum amount of DNA. In the present review, we collected data concerning the clinical application of NGS technology in the setting of colorectal cancer.

The possibility of clinical sequencing in the management of cancer

Comprehensive genomic profiling using next-generation sequencing technologies provides insights into understanding the genomic architecture of human cancer. This new understanding of the cancer genome allows us to identify many more genomic alterations occurring within tumors than before, some of which could be potential therapeutic targets through molecular targeted agents. Currently, a large number of molecular targeted agents are being developed, and consequently, cancer treatment is rapidly shifting from empiric therapy employing cytotoxic anticancer drugs to genotype-directed therapy using molecular targeted agents. In current daily clinical practice, hotspot-based single-gene assays that detect RAS mutations in colorectal cancer or EGFR mutations in non-small cell lung cancer are widely used to identify variants. However, it is becoming evident that more comprehensive genomic analysis is crucial in identifying the patient population that may benefit from molecular targeted therapy and the accelerated development of novel drugs for early clinical trials. For these purposes, an increasing number of gene panel-based targeted sequencing is commercially available in clinical practice from sequencing companies. Despite several challenges in implementing this approach, comprehensive genomic profiling and identification of actionable mutations is likely to become one of the standard options in the management of cancer in the near future. The use of clinical sequencing has the potential to usher a new era in precision medicine for cancer diagnosis and treatment. In this review, we discuss the application of comprehensive genomic profiling using next-generation sequencing technologies in clinical oncology and address the current challenges for its implementation.