Cancer genome sequencing: a review

Modification of the Hi-C Technology for Molecular Genetic Analysis of Formalin-Fixed Paraffin-Embedded Sections of Tumor Tissues

Molecular genetic analysis of tumor tissues is the most important step towards understanding the mechanisms of cancer development; it is also necessary for the choice of targeted therapy. The Hi-C (high-throughput chromatin conformation capture) technology can be used to detect various types of genomic variants, including balanced chromosomal rearrangements, such as inversions and translocations. We propose a modification of the Hi-C method for the analysis of chromatin contacts in formalin-fixed paraffin-embedded (FFPE) sections of tumor tissues. The developed protocol allows to generate high-quality Hi-C data and detect all types of chromosomal rearrangements. We have analyzed various databases to compile a comprehensive list of translocations that hold clinical importance for the targeted therapy selection. The practical value of molecular genetic testing is its ability to influence the treatment strategies and to provide prognostic insights. Detecting specific chromosomal rearrangements can guide the choice of the targeted therapies, which is a critical aspect of personalized medicine in oncology.

Sequential sequencing by synthesis and the next-generation sequencing revolution

The impact of next-generation sequencing (NGS) cannot be overestimated. The technology has transformed the field of life science, contributing to a dramatic expansion in our understanding of human health and disease and our understanding of biology and ecology. The vast majority of the major NGS systems today are based on the concept of 'sequencing by synthesis' (SBS) with sequential detection of nucleotide incorporation using an engineered DNA polymerase. Based on this strategy, various alternative platforms have been developed, including the use of either native nucleotides or reversible terminators and different strategies for the attachment of DNA to a solid support. In this review, some of the key concepts leading to this remarkable development are discussed.

ReConPlot: an R package for the visualization and interpretation of genomic rearrangements

MOTIVATION

Whole-genome sequencing studies of human tumours have revealed that complex forms of structural variation, collectively known as complex genome rearrangements (CGRs), are pervasive across diverse cancer types. Detection, classification, and mechanistic interpretation of CGRs requires the visualization of complex patterns of somatic copy number aberrations (SCNAs) and structural variants (SVs). However, there is a lack of tools specifically designed to facilitate the visualization and study of CGRs.

RESULTS

We present ReConPlot (REarrangement and COpy Number PLOT), an R package that provides functionalities for the joint visualization of SCNAs and SVs across one or multiple chromosomes. ReConPlot is based on the popular ggplot2 package, thus allowing customization of plots and the generation of publication-quality figures with minimal effort. Overall, ReConPlot facilitates the exploration, interpretation, and reporting of CGR patterns.

AVAILABILITY AND IMPLEMENTATION

The R package ReConPlot is available at https://github.com/cortes-ciriano-lab/ReConPlot. Detailed documentation and a tutorial with examples are provided with the package.

Testing for Phylogenetic Signal in Single-Cell RNA-Seq Data

Phylogenetic methods are emerging as a useful tool to understand cancer evolutionary dynamics, including tumor structure, heterogeneity, and progression. Most currently used approaches utilize either bulk whole genome sequencing or single-cell DNA sequencing and are based on calling copy number alterations and single nucleotide variants (SNVs). Single-cell RNA sequencing (scRNA-seq) is commonly applied to explore differential gene expression of cancer cells throughout tumor progression. The method exacerbates the single-cell sequencing problem of low yield per cell with uneven expression levels. This accounts for low and uneven sequencing coverage and makes SNV detection and phylogenetic analysis challenging. In this article, we demonstrate for the first time that scRNA-seq data contain sufficient evolutionary signal and can also be utilized in phylogenetic analyses. We explore and compare results of such analyses based on both expression levels and SNVs called from scRNA-seq data. Both techniques are shown to be useful for reconstructing phylogenetic relationships between cells, reflecting the clonal composition of a tumor. Both standardized expression values and SNVs appear to be equally capable of reconstructing a similar pattern of phylogenetic relationship. This pattern is stable even when phylogenetic uncertainty is taken in account. Our results open up a new direction of somatic phylogenetics based on scRNA-seq data. Further research is required to refine and improve these approaches to capture the full picture of somatic evolutionary dynamics in cancer.

A compilation of bioinformatic approaches to identify novel downstream targets for the detection and prophylaxis of cancer

The paradigm of cancer genomics has been radically changed by the development in next-generation sequencing (NGS) technologies making it possible to envisage individualized treatment based on tumor and stromal cells genome in a clinical setting within a short timeframe. The abundance of data has led to new avenues for studying coordinated alterations that impair biological processes, which in turn has increased the demand for bioinformatic tools for pathway analysis. While most of this work has been concentrated on optimizing certain algorithms to obtain quicker and more accurate results. Large volumes of these existing algorithm-based data are difficult for the biologists and clinicians to access, download and reanalyze them. In the present study, we have listed the bioinformatics algorithms and user-friendly graphical user interface (GUI) tools that enable code-independent analysis of big data without compromising the quality and time. We have also described the advantages and drawbacks of each of these platforms. Additionally, we emphasize the importance of creating new, more user-friendly solutions to provide better access to open data and talk about relevant problems like data sharing and patient privacy.

Early Application of Next-Generation Sequencing Identifies Pancreatic Mass as Metastasis From an EGFR-Mutated Lung Adenocarcinoma

Pancreatic metastasis of primary lung adenocarcinoma is a rare occurrence, accounting for <0.3% of all pancreatic malignancies. Given that the prognosis and treatment options for primary pancreatic cancer differ greatly from pancreatic metastases from a primary site, an accurate diagnosis is critical. This report presents a unique case of a 65-year-old man who was admitted with significant unintentional weight loss, fatigue, abdominal pain, and jaundice, and found to have a pancreatic mass initially thought to be primary pancreatic adenocarcinoma and subsequently diagnosed as an EGFR-mutated lung adenocarcinoma with metastases to the pancreas via early application of next-generation sequencing (NGS). The use of NGS early in the patient's clinical course not only changed the treatment strategy but also drastically altered the prognosis. Although metastatic pancreatic adenocarcinoma has a poor prognosis and survival rate, treatment of EGFR-mutated non-small cell lung cancer with EGFR tyrosine kinase inhibitors is associated with high response rates. Importantly, our case demonstrates that timely application of NGS very early in the disease course is paramount to the diagnosis, management, and prognosis of solid malignancies.

The Multi-Omic Landscape of Primary Breast Tumors and Their Metastases: Expanding the Efficacy of Actionable Therapeutic Targets

Breast cancer (BC) mortality is almost exclusively due to metastasis, which is the least understood aspect of cancer biology and represents a significant clinical challenge. Although we have witnessed tremendous advancements in the treatment for metastatic breast cancer (mBC), treatment resistance inevitably occurs in most patients. Recently, efforts in characterizing mBC revealed distinctive genomic, epigenomic and transcriptomic (multi-omic) landscapes to that of the primary tumor. Understanding of the molecular underpinnings of mBC is key to understanding resistance to therapy and the development of novel treatment options. This review summarizes the differential molecular landscapes of BC and mBC, provides insights into the genomic heterogeneity of mBC and highlights the therapeutically relevant, multi-omic features that may serve as novel therapeutic targets for mBC patients.

TAaCGH Suite for Detecting Cancer-Specific Copy Number Changes Using Topological Signatures

Copy number changes play an important role in the development of cancer and are commonly associated with changes in gene expression. Persistence curves, such as Betti curves, have been used to detect copy number changes; however, it is known these curves are unstable with respect to small perturbations in the data. We address the stability of lifespan and Betti curves by providing bounds on the distance between persistence curves of Vietoris–Rips filtrations built on data and slightly perturbed data in terms of the bottleneck distance. Next, we perform simulations to compare the predictive ability of Betti curves, lifespan curves (conditionally stable) and stable persistent landscapes to detect copy number aberrations. We use these methods to identify significant chromosome regions associated with the four major molecular subtypes of breast cancer: Luminal A, Luminal B, Basal and HER2 positive. Identified segments are then used as predictor variables to build machine learning models which classify patients as one of the four subtypes. We find that no single persistence curve outperforms the others and instead suggest a complementary approach using a suite of persistence curves. In this study, we identified new cytobands associated with three of the subtypes: 1q21.1-q25.2, 2p23.2-p16.3, 23q26.2-q28 with the Basal subtype, 8p22-p11.1 with Luminal B and 2q12.1-q21.1 and 5p14.3-p12 with Luminal A. These segments are validated by the TCGA BRCA cohort dataset except for those found for Luminal A.

EagleC: A deep-learning framework for detecting a full range of structural variations from bulk and single-cell contact maps

The Hi-C technique has been shown to be a promising method to detect structural variations (SVs) in human genomes. However, algorithms that can use Hi-C data for a full-range SV detection have been severely lacking. Current methods can only identify interchromosomal translocations and long-range intrachromosomal SVs (>1 Mb) at less-than-optimal resolution. Therefore, we develop EagleC, a framework that combines deep-learning and ensemble-learning strategies to predict a full range of SVs at high resolution. We show that EagleC can uniquely capture a set of fusion genes that are missed by whole-genome sequencing or nanopore. Furthermore, EagleC also effectively captures SVs in other chromatin interaction platforms, such as HiChIP, Chromatin interaction analysis with paired-end tag sequencing (ChIA-PET), and capture Hi-C. We apply EagleC in more than 100 cancer cell lines and primary tumors and identify a valuable set of high-quality SVs. Last, we demonstrate that EagleC can be applied to single-cell Hi-C and used to study the SV heterogeneity in primary tumors.

A Deep Learning-Based Framework for Supporting Clinical Diagnosis of Glioblastoma Subtypes

Understanding molecular features that facilitate aggressive phenotypes in glioblastoma multiforme (GBM) remains a major clinical challenge. Accurate diagnosis of GBM subtypes, namely classical, proneural, and mesenchymal, and identification of specific molecular features are crucial for clinicians for systematic treatment. We develop a biologically interpretable and highly efficient deep learning framework based on a convolutional neural network for subtype identification. The classifiers were generated from high-throughput data of different molecular levels, i.e., transcriptome and methylome. Furthermore, an integrated subsystem of transcriptome and methylome data was also used to build the biologically relevant model. Our results show that deep learning model outperforms the traditional machine learning algorithms. Furthermore, to evaluate the biological and clinical applicability of the classification, we performed weighted gene correlation network analysis, gene set enrichment, and survival analysis of the feature genes. We identified the genotype-phenotype relationship of GBM subtypes and the subtype-specific predictive biomarkers for potential diagnosis and treatment.

A review on the genetic polymorphisms and susceptibility of cancer patients in Bangladesh

Cancer is one of the major health burdens worldwide, and genetic polymorphisms in individuals are closely associated with cancer susceptibility. Like in many other developing countries, the risk of cancer is increasing among Bangladeshi population. Genetic polymorphisms in xenobiotic metabolic enzymes (CYP1A1, CYP2A6, CYP3A4, CYP3A5, NAT2, SULT1A), cell cycle regulatory proteins (TP53, HER2, MDM2, miR-218-2, TGFB), cell signaling protein (CDH1), DNA repair proteins (BRCA1, BRCA2, EXO1, RAD51, XRCC2, ECCR1, ERCC4, XPC, ERCC2), and others (HLA-DRB1, INSIG2, GCNT1P5) have been found to be associated with various cancers like cancers of breast, bladder, cervix, colon, lung, prostate, etc. in different studies with Bangladeshi population. In this review article, we have discussed these gene polymorphisms associated with cancers in the Bangladeshi population, and also made a comparison with other ethnic groups. This will probably be helpful in understanding drug effects, drug resistance, and personalized medicine in the population of this region.

Liquid Biopsy as a Prognostic and Theranostic Tool for the Management of Pancreatic Ductal Adenocarcinoma

Pancreatic ductal adenocarcinomas (PDAC) represent one of the deadliest cancers worldwide. Survival is still low due to diagnosis at an advanced stage and resistance to treatment. Herein, we review the main types of liquid biopsy able to help in both prognosis and adaptation of treatments.

Topology preserving stratification of tissue neoplasticity using Deep Neural Maps and microRNA signatures

Background

Accurate cancer classification is essential for correct treatment selection and better prognostication. microRNAs (miRNAs) are small RNA molecules that negatively regulate gene expression, and their dyresgulation is a common disease mechanism in many cancers. Through a clearer understanding of miRNA dysregulation in cancer, improved mechanistic knowledge and better treatments can be sought.

Results

We present a topology-preserving deep learning framework to study miRNA dysregulation in cancer. Our study comprises miRNA expression profiles from 3685 cancer and non-cancer tissue samples and hierarchical annotations on organ and neoplasticity status. Using unsupervised learning, a two-dimensional topological map is trained to cluster similar tissue samples. Labelled samples are used after training to identify clustering accuracy in terms of tissue-of-origin and neoplasticity status. In addition, an approach using activation gradients is developed to determine the attention of the networks to miRNAs that drive the clustering. Using this deep learning framework, we classify the neoplasticity status of held-out test samples with an accuracy of 91.07%, the tissue-of-origin with 86.36%, and combined neoplasticity status and tissue-of-origin with an accuracy of 84.28%. The topological maps display the ability of miRNAs to recognize tissue types and neoplasticity status. Importantly, when our approach identifies samples that do not cluster well with their respective classes, activation gradients provide further insight in cancer subtypes or grades.

Conclusions

An unsupervised deep learning approach is developed for cancer classification and interpretation. This work provides an intuitive approach for understanding molecular properties of cancer and has significant potential for cancer classification and treatment selection.

Analysis of Hi-C Data for Discovery of Structural Variations in Cancer

Structural variations (SVs) are large genomic rearrangements that can be challenging to identify with current short read sequencing technology due to various confounding factors such as existence of genomic repeats and complex SV structures. Hi-C breakfinder is the first computational tool that utilizes the technology of high-throughput chromatin conformation capture assay (Hi-C) to systematically identify SVs, without being interfered by regular confounding factors. SVs change the spatial distance of genomic regions and cause discontinuous signals in Hi-C, which are difficult to analyze by routine informatics practice. Here we provide step-by-step guidance for how to identify SVs using Hi-C data and how to reconstruct Hi-C maps in the presence of SVs.

Whole-Genome Profiles of Malay Colorectal Cancer Patients with Intact MMR Proteins

Background: This study aimed to identify new genes associated with CRC in patients with normal mismatch repair (MMR) protein expression. Method: Whole-genome sequencing (WGS) was performed in seven early-age-onset Malay CRC patients. Potential germline genetic variants, including single-nucleotide variations and insertions and deletions (indels), were prioritized using functional and predictive algorithms. Results: An average of 3.2 million single-nucleotide variations (SNVs) and over 800 indels were identified. Three potential candidate variants in three genes—IFNE, PTCH2 and SEMA3D—which were predicted to affect protein function, were identified in three Malay CRC patients. In addition, 19 candidate genes—ANKDD1B, CENPM, CLDN5, MAGEB16, MAP3K14, MOB3C, MS4A12, MUC19, OR2L8, OR51Q1, OR51AR1, PDE4DIP, PKD1L3, PRIM2, PRM3, SEC22B, TPTE, USP29 and ZNF117—harbouring nonsense variants were prioritised. These genes are suggested to play a role in cancer predisposition and to be associated with cancer risk. Pathway enrichment analysis indicated significant enrichment in the olfactory signalling pathway. Conclusion: This study provides a new spectrum of insights into the potential genes, variants and pathways associated with CRC in Malay patients.

Oral Cancer: Genetics and the Role of Precision Medicine

Oral squamous cell carcinoma (OSCC) is one of the leading cancers in the world. OSCC patients are managed with surgery and/or chemoradiation. Prognoses and survival rates are dismal, however, and have not improved for more than 20 years. Recently, the concept of precision medicine was introduced, and the introduction of targeted therapeutics demonstrated promising outcomes. This article reviews the current understanding of initiation, progression, and metastasis of OSCC from both genetic and epigenetic perspectives. In addition, the applications and integration of omics technologies in biomarker discovery and drug development for treating OSCC are reviewed.

Structural Variations of the 3D Genome Architecture in Cervical Cancer Development

Human papillomavirus (HPV) integration is the major contributor to cervical cancer (CC) development by inducing structural variations (SVs) in the human genome. SVs are directly associated with the three-dimensional (3D) genome structure leading to cancer development. The detection of SVs is not a trivial task, and several genome-wide techniques have greatly helped in the identification of SVs in the cancerous genome. However, in cervical cancer, precise prediction of SVs mainly translocations and their effects on 3D-genome and gene expression still need to be explored. Here, we have used high-throughput chromosome conformation capture (Hi-C) data of cervical cancer to detect the SVs, especially the translocations, and validated it through whole-genome sequencing (WGS) data. We found that the cervical cancer 3D-genome architecture rearranges itself as compared to that in the normal tissue, and 24% of the total genome switches their A/B compartments. Moreover, translocation detection from Hi-C data showed the presence of high-resolution t(4;7) (q13.1; q31.32) and t(1;16) (q21.2; q22.1) translocations, which disrupted the expression of the genes located at and nearby positions. Enrichment analysis suggested that the disrupted genes were mainly involved in controlling cervical cancer-related pathways. In summary, we detect the novel SVs through Hi-C data and unfold the association among genome-reorganization, translocations, and gene expression regulation. The results help understand the underlying pathogenicity mechanism of SVs in cervical cancer development and identify the targeted therapeutics against cervical cancer.

Tumor heterogeneity assessed by sequencing and fluorescence in situ hybridization (FISH) data

MOTIVATION

Computational reconstruction of clonal evolution in cancers has become a crucial tool for understanding how tumors initiate and progress and how this process varies across patients. The field still struggles, however, with special challenges of applying phylogenetic methods to cancers, such as the prevalence and importance of copy number alteration (CNA) and structural variation (SV) events in tumor evolution, which are difficult to profile accurately by prevailing sequencing methods in such a way that subsequent reconstruction by phylogenetic inference algorithms is accurate.

RESULTS

In the present work, we develop computational methods to combine sequencing with multiplex interphase fluorescence in situ hybridization (miFISH) to exploit the complementary advantages of each technology in inferring accurate models of clonal CNA evolution accounting for both focal changes and aneuploidy at whole-genome scales. By integrating such information in an integer linear programming (ILP) framework, we demonstrate on simulated data that incorporation of FISH data substantially improves accurate inference of focal CNA and ploidy changes in clonal evolution from deconvolving bulk sequence data. Analysis of real glioblastoma data for which FISH, bulk sequence, and single cell sequence are all available confirms the power of FISH to enhance accurate reconstruction of clonal copy number evolution in conjunction with bulk and optionally single-cell sequence data.

AVAILABILITY

Source code is available on Github at https://github.com/CMUSchwartzLab/FISH_deconvolution.

Impact of Endoscopic Ultrasound-Guided Tissue Acquisition on Decision-Making in Precision Medicine for Pancreatic Cancer: Beyond Diagnosis

Precision medicine in cancer treatment refers to targeted therapy based on the evaluation of biomarkers. Although precision medicine for pancreatic cancer (PC) remains challenging, novel biomarker-based therapies, such as pembrolizumab, olaparib, and entrectinib, have been emerging. Most commonly, endoscopic ultrasound-guided tissue acquisition (EUS-TA) had been used for the diagnosis of PC until now. However, advances in EUS-TA devices and biomarker testing, especially next-generation sequencing, have opened up the possibility of sequencing of various genes even in limited amounts of tissue samples obtained by EUS-TA, and identifying potential genetic alterations as therapeutic targets. Precision medicine benefits only a small population of patients with PC, but biomarker-based therapy has shown promising results in patients who once had no treatment options. Now, the role of EUS-TA has extended beyond diagnosis into decision-making regarding the treatment of PC. In this review, we mainly discuss tissue sampling by EUS-TA for biomarker testing and the current status of precision medicine for PC.

Clinical outcomes of chemotherapy in patients with undifferentiated carcinoma of the pancreas: a retrospective multicenter cohort study

BACKGROUND

Undifferentiated carcinoma (UC) of the pancreas is a rare subtype of pancreatic cancer. Although UC has been considered a highly aggressive malignancy, no clinical studies have addressed the efficacy of chemotherapy for unresectable UC. Therefore, we conducted multicenter retrospective study to investigate the efficacy of chemotherapy in patients with UC of the pancreas.

METHODS

This multicenter retrospective cohort study was conducted at 17 institutions in Japan between January 2007 and December 2017. A total of 50 patients treated with chemotherapy were analyzed.

RESULTS

The median overall survival (OS) in UC patients treated with chemotherapy was 4.08 months. The details of first-line chemotherapy were as follows: gemcitabine (n = 24), S-1 (n = 12), gemcitabine plus nab-paclitaxel (n = 6), and other treatment (n = 8). The median progression-free survival (PFS) was 1.61 months in the gemcitabine group, 2.96 months in the S-1 group, and 4.60 months in the gemcitabine plus nab-paclitaxel group. Gemcitabine plus nab-paclitaxel significantly improved PFS compared with gemcitabine (p = 0.014). The objective response rate (ORR) was 4.2% in the gemcitabine group, 0.0% in the S-1 group, and 33.3% in the gemcitabine plus nab-paclitaxel group. Gemcitabine plus nab-paclitaxel also showed a significantly higher ORR compared with both gemcitabine and S-1 (gemcitabine plus nab-paclitaxel vs. gemcitabine: p = 0.033; gemcitabine plus nab-paclitaxel vs. S-1: p = 0.034). A paclitaxel-containing first-line regimen significantly improved OS compared with a non-paclitaxel-containing regimen (6.94 months vs. 3.75 months, respectively; p = 0.041). After adjustment, use of a paclitaxel-containing regimen in any line was still an independent predictor of OS (hazard ratio for OS, 0.221; 95% confidence interval, 0.076-0.647; p = 0.006) in multiple imputation by chained equation.

CONCLUSIONS

The results of the present study indicate that a paclitaxel-containing regimen would offer relatively longer survival, and it is considered a reasonable option for treating patients with unresectable UC.

A Bayes factor approach with informative prior for rare genetic variant analysis from next generation sequencing data

The discovery of rare genetic variants through next generation sequencing is a very challenging issue in the field of human genetics. We propose a novel region-based statistical approach based on a Bayes Factor (BF) to assess evidence of association between a set of rare variants (RVs) located on the same genomic region and a disease outcome in the context of case-control design. Marginal likelihoods are computed under the null and alternative hypotheses assuming a binomial distribution for the RV count in the region and a beta or mixture of Dirac and beta prior distribution for the probability of RV. We derive the theoretical null distribution of the BF under our prior setting and show that a Bayesian control of the false Discovery Rate can be obtained for genome-wide inference. Informative priors are introduced using prior evidence of association from a Kolmogorov-Smirnov test statistic. We use our simulation program, sim1000G, to generate RV data similar to the 1000 genomes sequencing project. Our simulation studies showed that the new BF statistic outperforms standard methods (SKAT, SKAT-O, Burden test) in case-control studies with moderate sample sizes and is equivalent to them under large sample size scenarios. Our real data application to a lung cancer case-control study found enrichment for RVs in known and novel cancer genes. It also suggests that using the BF with informative prior improves the overall gene discovery compared to the BF with noninformative prior.

Single-molecule analysis of nucleic acid biomarkers - A review

Nucleic acids are important biomarkers for disease detection, monitoring, and treatment. Advances in technologies for nucleic acid analysis have enabled discovery and clinical implementation of nucleic acid biomarkers. However, challenges remain with technologies for nucleic acid analysis, thereby limiting the use of nucleic acid biomarkers in certain contexts. Here, we review single-molecule technologies for nucleic acid analysis that can be used to overcome these challenges. We first discuss the various types of nucleic acid biomarkers important for clinical applications and conventional technologies for nucleic acid analysis. We then discuss technologies for single-molecule in vitro and in situ analysis of nucleic acid biomarkers. Finally, we discuss other ultra-sensitive techniques for nucleic acid biomarker detection.

Evolving paradigms on the interplay of mitochondrial Hsp70 chaperone system in cell survival and senescence

The role of mitochondria within a cell has grown beyond being the prime source of cellular energy to one of the major signaling platforms. Recent evidence provides several insights into the crucial roles of mitochondrial chaperones in regulating the organellar response to external triggers. The mitochondrial Hsp70 (mtHsp70/Mortalin/Grp75) chaperone system plays a critical role in the maintenance of proteostasis balance in the organelle. Defects in mtHsp70 network result in attenuated protein transport and misfolding of polypeptides leading to mitochondrial dysfunction. The functions of Hsp70 are primarily governed by J-protein cochaperones. Although human mitochondria possess a single Hsp70, its multifunctionality is characterized by the presence of multiple specific J-proteins. Several studies have shown a potential association of Hsp70 and J-proteins with diverse pathological states that are not limited to their canonical role as chaperones. The role of mitochondrial Hsp70 and its co-chaperones in disease pathogenesis has not been critically reviewed in recent years. We evaluated some of the cellular interfaces where Hsp70 machinery associated with pathophysiological conditions, particularly in context of tumorigenesis and neurodegeneration. The mitochondrial Hsp70 machinery shows a variable localization and integrates multiple components of the cellular processes with varied phenotypic consequences. Although Hsp70 and J-proteins function synergistically in proteins folding, their precise involvement in pathological conditions is mainly idiosyncratic. This machinery is associated with a heterogeneous set of molecules during the progression of a disorder. However, the precise binding to the substrate for a specific physiological response under a disease subtype is still an undocumented area of analysis.

Targeting Metabolic Reprogramming in Acute Myeloid Leukemia

The cancer metabolic reprogramming allows the maintenance of tumor proliferation, expansion and survival by altering key bioenergetics, biosynthetic and redox functions to meet the higher demands of tumor cells. In addition, several metabolites are also needed to perform signaling functions that further promote tumor growth and progression. These metabolic alterations have been exploited in different cancers, including acute myeloid leukemia, as novel therapeutic strategies both in preclinical models and clinical trials. Here, we review the complexity of acute myeloid leukemia (AML) metabolism and discuss how therapies targeting different aspects of cellular metabolism have demonstrated efficacy and how they provide a therapeutic window that should be explored to target the metabolic requirements of AML cells.

New Era of Endoscopic Ultrasound-Guided Tissue Acquisition: Next-Generation Sequencing by Endoscopic Ultrasound-Guided Sampling for Pancreatic Cancer

Pancreatic cancer is a lethal cancer with an increasing incidence. Despite improvements in chemotherapy, patients with pancreatic cancer continue to face poor prognoses. Endoscopic ultrasound-guided tissue acquisition (EUS-TA) is the primary method for obtaining tissue samples of pancreatic cancer. Due to advancements in next-generation sequencing (NGS) technologies, multiple parallel sequencing can be applied to EUS-TA samples. Genomic biomarkers for therapeutic stratification in pancreatic cancer are still lacking, however, NGS can unveil potential predictive genomic biomarkers of treatment response. Thus, the importance of NGS using EUS-TA samples is becoming recognized. In this review, we discuss the recent advances in EUS-TA application for NGS of pancreatic cancer.

A novel knowledge-derived data potentizing method revealed unique liver cancer-associated genetic variants

Background

Next-generation sequencing (NGS) has been advancing the progress of detection of disease-associated genetic variants and genome-wide profiling of expressed sequences over the past decade. NGS enables the analyses of multiple regions of a genome in a single reaction format and has been shown to be a cost-effective and efficient tool for root-cause analysis of disease and optimization of treatment. NGS has been leading global efforts to device personalized and precision medicine (PM) in clinical practice. The effectiveness of NGS for the aforementioned applications has been proven unequivocal for multifactorial diseases like cancer. However, definitive prediction of cancer markers for all types of diseases and for global populations still remains highly rewarding because of the diversity of cancer types and genetic variants in human.

Results

We performed exome sequencing of four samples in quest of critical genetic factor/s associated with liver cancer. By imposing knowledge-based filter chains, we have revealed a panel of genetic variants, which are unrecognized by current major genomics data repositories. Total 20 MNV-induced, 5 INDEL-induced, and 31 SNV-induced neoplasm-exclusive genes were revealed through NGS data acquisition followed by data curing with the application of quality filter chains. Liver-specific expression profile of the identified gene pool is directed to the selection of 17 genes which could be the as likely causative genetic factors for liver cancer. Further study on expression level and relevant functional significance enables us to identify and conclude the following four novel variants, viz., c.416T>C (p.Phe139Ser) in SORD, c.1048_1049delGCinsCG (p.Ala350Arg) in KRT6A, c.1159G>T (p.Gly387Cys) in SVEP1, and c.430G>C (p.Gly144Arg) in MRPL38 as a critical genetic factor for liver cancer.

Conclusion

By applying a novel data prioritizing rationale, we explored a panel of previously unaddressed liver cancer-associated variants. These findings may have an opportunity for early prediction of neoplasm/cancer in liver and designing of relevant personalized/precision liver cancer therapeutics in clinical practice. Since NGS protocol is associated with tons of non-specific mutations due to the variation in background genetic makeup of subjects, therefore, our method of data curing could be applicable for more effective screening of global genetic variants related to disease onset, progression, and remission.

Electronic supplementary material

The online version of this article (10.1186/s40246-019-0213-7) contains supplementary material, which is available to authorized users.

Personal genome testing on physicians improves attitudes on pharmacogenomic approaches

In this era of clinical genomics, the accumulation of knowledge of pharmacogenomics (PGx) is rising dramatically and attempts to utilize it in clinical practice are also increasing. However, this advanced knowledge and information have not yet been sufficiently utilized in the clinical field due to various barriers including physician factors. This study was conducted to evaluate the attitudes of physicians to PGx services by providing them their own genomic data analysis report focusing on PGx. We also tried to evaluate the clinical applicability of whole exome sequencing (WES)-based functional PGx test. In total 88 physicians participated in the study from September 2015 to August 2016. Physicians who agreed to participate in the study were asked to complete a pre-test survey evaluating their knowledge of and attitude toward clinical genomics including PGx. Only those who completed the pre-test survey proceeded to WES and were provided with a personal PGx analysis report in an offline group meeting. Physicians who received these PGx reports were asked to complete a follow-up survey within two weeks. We then analyzed changes in their knowledge and attitude after reviewing their own PGx analysis results through differences in their pre-test and post-test survey responses. In total, 70 physicians (79.5%) completed the pre-test and post-test surveys and attended an off-line seminar to review their personal PGx reports. After physicians reviewed the report, their perception of and attitude towards the PGx domain and genomics significantly changed. Physician’ awareness of the likelihood of occurrence of adverse drug reactions and genetic contribution was also changed significantly. Overall, physicians were very positive about the value and potential of the PGx test but maintained a conservative stance on its actual clinical use. Results revealed that physicians’ perception and attitude to the utility of PGx testing was significantly changed after reviewing their own WES results.

Stable Isotope Labeling Highlights Enhanced Fatty Acid and Lipid Metabolism in Human Acute Myeloid Leukemia

Background: In Acute Myeloid Leukemia (AML), a complete response to chemotherapy is usually obtained after conventional chemotherapy but overall patient survival is poor due to highly frequent relapses. As opposed to chronic myeloid leukemia, B lymphoma or multiple myeloma, AML is one of the rare malignant hemopathies the therapy of which has not significantly improved during the past 30 years despite intense research efforts. One promising approach is to determine metabolic dependencies in AML cells. Moreover, two key metabolic enzymes, isocitrate dehydrogenases (IDH1/2), are mutated in more than 15% of AML patient, reinforcing the interest in studying metabolic reprogramming, in particular in this subgroup of patients. Methods: Using a multi-omics approach combining proteomics, lipidomics, and isotopic profiling of [U-¹³C] glucose and [U-¹³C] glutamine cultures with more classical biochemical analyses, we studied the impact of the IDH1 R132H mutation in AML cells on lipid biosynthesis. Results: Global proteomic and lipidomic approaches showed a dysregulation of lipid metabolism, especially an increase of phosphatidylinositol, sphingolipids (especially few species of ceramide, sphingosine, and sphinganine), free cholesterol and monounsaturated fatty acids in IDH1 mutant cells. Isotopic profiling of fatty acids revealed that higher lipid anabolism in IDH1 mutant cells corroborated with an increase in lipogenesis fluxes. Conclusions: This integrative approach was efficient to gain insight into metabolism and dynamics of lipid species in leukemic cells. Therefore, we have determined that lipid anabolism is strongly reprogrammed in IDH1 mutant AML cells with a crucial dysregulation of fatty acid metabolism and fluxes, both being mediated by 2-HG (2-Hydroxyglutarate) production.

Maftools: efficient and comprehensive analysis of somatic variants in cancer

Numerous large-scale genomic studies of matched tumor-normal samples have established the somatic landscapes of most cancer types. However, the downstream analysis of data from somatic mutations entails a number of computational and statistical approaches, requiring usage of independent software and numerous tools. Here, we describe an R Bioconductor package, Maftools, which offers a multitude of analysis and visualization modules that are commonly used in cancer genomic studies, including driver gene identification, pathway, signature, enrichment, and association analyses. Maftools only requires somatic variants in Mutation Annotation Format (MAF) and is independent of larger alignment files. With the implementation of well-established statistical and computational methods, Maftools facilitates data-driven research and comparative analysis to discover novel results from publicly available data sets. In the present study, using three of the well-annotated cohorts from The Cancer Genome Atlas (TCGA), we describe the application of Maftools to reproduce known results. More importantly, we show that Maftools can also be used to uncover novel findings through integrative analysis.

Integrative detection and analysis of structural variation in cancer genomes

Structural variants (SVs) can contribute to oncogenesis through a variety of mechanisms. Despite their importance, the identification of SVs in cancer genomes remains challenging. Here, we present a framework that integrates optical mapping, high-throughput chromosome conformation capture (Hi-C), and whole-genome sequencing to systematically detect SVs in a variety of normal or cancer samples and cell lines. We identify the unique strengths of each method and demonstrate that only integrative approaches can comprehensively identify SVs in the genome. By combining Hi-C and optical mapping, we resolve complex SVs and phase multiple SV events to a single haplotype. Furthermore, we observe widespread structural variation events affecting the functions of noncoding sequences, including the deletion of distal regulatory sequences, alteration of DNA replication timing, and the creation of novel three-dimensional chromatin structural domains. Our results indicate that noncoding SVs may be underappreciated mutational drivers in cancer genomes.

Oral Cancer: Genetics and the Role of Precision Medicine

Oral squamous cell carcinoma (OSCC) is one of the leading cancers in the world. OSCC patients are managed with surgery and/or chemoradiation. Prognoses and survival rates are dismal, however, and have not improved for more than 20 years. Recently, the concept of precision medicine was introduced, and the introduction of targeted therapeutics demonstrated promising outcomes. This article reviews the current understanding of initiation, progression, and metastasis of OSCC from both genetic and epigenetic perspectives. In addition, the applications and integration of omics technologies in biomarker discovery and drug development for treating OSCC are reviewed.

Update on the types and usage of liquid biopsies in the clinical setting: a systematic review

Background

This systematic review aimed to gather evidence from research on the current state of liquid biopsy in medical practice, specifically focusing on mutation detection and monitoring.

Methods

A systematic search was performed via Medline.

Results

The results of this investigation indicate that liquid biopsy plays a critical role in the detection and management of tumors. This technique gives healthcare providers the ability to gather critical and reliable information that may potentially shape the diagnosis, treatment, and prognosis of a variety of cancers in the near future. This study further reveals that liquid biopsy has several potential shortcomings that may limit its application and use in the healthcare setting. Nevertheless, liquid biopsy remains a valuable tool that is gradually becoming a part of routine healthcare practice in oncology departments and hospitals worldwide.

Conclusions

The evidence described herein reveals the potential relevance of liquid biopsy as an important prognostic, diagnostic, and theranostic tool. This non-invasive procedure enables healthcare practitioners to detect and monitor genomic alterations and will likely replace tumor tissue biopsy as the standard method for detecting and monitoring mutations in the future. The information obtained herein can enable physicians to make informed decisions regarding current treatment options; however, liquid biopsy has not yet been incorporated into routine clinical diagnostics for cancer patients.

Mitochondrial determinants of cancer health disparities

Mitochondria, which are multi-functional, have been implicated in cancer initiation, progression, and metastasis due to metabolic alterations in transformed cells. Mitochondria are involved in the generation of energy, cell growth and differentiation, cellular signaling, cell cycle control, and cell death. To date, the mitochondrial basis of cancer disparities is unknown. The goal of this review is to provide an understanding and a framework of mitochondrial determinants that may contribute to cancer disparities in racially different populations. Due to maternal inheritance and ethnic-based diversity, the mitochondrial genome (mtDNA) contributes to inherited racial disparities. In people of African ancestry, several germline, population-specific haplotype variants in mtDNA as well as depletion of mtDNA have been linked to cancer predisposition and cancer disparities. Indeed, depletion of mtDNA and mutations in mtDNA or nuclear genome (nDNA)-encoded mitochondrial proteins lead to mitochondrial dysfunction and promote resistance to apoptosis, the epithelial-to-mesenchymal transition, and metastatic disease, all of which can contribute to cancer disparity and tumor aggressiveness related to racial disparities. Ethnic differences at the level of expression or genetic variations in nDNA encoding the mitochondrial proteome, including mitochondria-localized mtDNA replication and repair proteins, miRNA, transcription factors, kinases and phosphatases, and tumor suppressors and oncogenes may underlie susceptibility to high-risk and aggressive cancers found in African population and other ethnicities. The mitochondrial retrograde signaling that alters the expression profile of nuclear genes in response to dysfunctional mitochondria is a mechanism for tumorigenesis. In ethnic populations, differences in mitochondrial function may alter the cross talk between mitochondria and the nucleus at epigenetic and genetic levels, which can also contribute to cancer health disparities. Targeting mitochondrial determinants and mitochondrial retrograde signaling could provide a promising strategy for the development of selective anticancer therapy for dealing with cancer disparities. Further, agents that restore mitochondrial function to optimal levels should permit sensitivity to anticancer agents for the treatment of aggressive tumors that occur in racially diverse populations and hence help in reducing racial disparities.

Highly accurate fluorogenic DNA sequencing with information theory-based error correction

Eliminating errors in next-generation DNA sequencing has proved challenging. Here we present error-correction code (ECC) sequencing, a method to greatly improve sequencing accuracy by combining fluorogenic sequencing-by-synthesis (SBS) with an information theory-based error-correction algorithm. ECC embeds redundancy in sequencing reads by creating three orthogonal degenerate sequences, generated by alternate dual-base reactions. This is similar to encoding and decoding strategies that have proved effective in detecting and correcting errors in information communication and storage. We show that, when combined with a fluorogenic SBS chemistry with raw accuracy of 98.1%, ECC sequencing provides single-end, error-free sequences up to 200 bp. ECC approaches should enable accurate identification of extremely rare genomic variations in various applications in biology and medicine.

A NOVEL AND EFFICIENT ALGORITHM FOR DE NOVO DISCOVERY OF MUTATED DRIVER PATHWAYS IN CANCER

Next-generation sequencing studies on cancer somatic mutations have discovered that driver mutations tend to appear in most tumor samples, but they barely overlap in any single tumor sample, presumably because a single driver mutation can perturb the whole pathway. Based on the corresponding new concepts of coverage and mutual exclusivity, new methods can be designed for de novo discovery of mutated driver pathways in cancer. Since the computational problem is a combinatorial optimization with an objective function involving a discontinuous indicator function in high dimension, many existing optimization algorithms, such as a brute force enumeration, gradient descent and Newton's methods, are practically infeasible or directly inapplicable. We develop a new algorithm based on a novel formulation of the problem as non-convex programming and non-convex regularization. The method is computationally more efficient, effective and scalable than existing Monte Carlo searching and several other algorithms, which have been applied to The Cancer Genome Atlas (TCGA) project. We also extend the new method for integrative analysis of both mutation and gene expression data. We demonstrate the promising performance of the new methods with applications to three cancer datasets to discover de novo mutated driver pathways.

Unravelling biology and shifting paradigms in cancer with single-cell sequencing

The fundamental operative unit of a cancer is the genetically and epigenetically innovative single cell. Whether proliferating or quiescent, in the primary tumour mass or disseminated elsewhere, single cells govern the parameters that dictate all facets of the biology of cancer. Thus, single-cell analyses provide the ultimate level of resolution in our quest for a fundamental understanding of this disease. Historically, this quest has been hampered by technological shortcomings. In this Opinion article, we argue that the rapidly evolving field of single-cell sequencing has unshackled the cancer research community of these shortcomings. From furthering an elemental understanding of intra-tumoural genetic heterogeneity and cancer genome evolution to illuminating the governing principles of disease relapse and metastasis, we posit that single-cell sequencing promises to unravel the biology of all facets of this disease.

Employing RNA viruses to fight cancer: novel insights into oncolytic virotherapy

Within recent decades, viruses that specifically target tumor cells have emerged as novel therapeutic agents against cancer. These viruses do not only act via their cell-lytic properties, but also harbor immunostimulatory features to re-direct the tumor microenvironment and stimulate tumor-directed immune responses. Furthermore, oncolytic viruses are considered to be superior to classical cancer therapies due to higher selectivity towards tumor cell destruction and, consequently, less collateral damage of non-transformed healthy tissue. In particular, the field of oncolytic RNA viruses is rapidly developing since these agents possess alternative tumor-targeting strategies compared to established oncolytic DNA viruses. Thus, oncolytic RNA viruses have broadened the field of virotherapy facilitating new strategies to fight cancer. In addition to several naturally occurring oncolytic viruses, genetically modified RNA viruses that are armed to express foreign factors such as immunostimulatory molecules have been successfully tested in early clinical trials showing promising efficacy. This review aims to provide an overview of the most promising RNA viruses in clinical development, to summarize the current knowledge of clinical trials using these viral agents, and to discuss the main issues as well as future perspectives of clinical approaches using oncolytic RNA viruses.

Intersect-then-combine approach: improving the performance of somatic variant calling in whole exome sequencing data using multiple aligners and callers

Bioinformatic analysis of genomic sequencing data to identify somatic mutations in cancer samples is far from achieving the required robustness and standardisation. In this study we generated a whole exome sequencing benchmark dataset using the platinum genome sample NA12878 and developed an intersect-then-combine (ITC) approach to increase the accuracy in calling single nucleotide variants (SNVs) and indels in tumour-normal pairs. We evaluated the effect of alignment, base quality recalibration, mutation caller and filtering on sensitivity and false positive rate. The ITC approach increased the sensitivity up to 17.1%, without increasing the false positive rate per megabase (FPR/Mb) and its validity was confirmed in a set of clinical samples.

Mapping Billions of Short Reads to a Reference Genome

Rapid development and commercialization of instruments that can accurately, rapidly, and cheaply sequence billions of DNA bases is revolutionizing molecular biology and medicine. Because a reference genome is usually available, the first bioinformatics challenge presented by the new generation of high-throughput sequencers is the genome mapping problem, where each read is mapped to a reference genome to reveal its location(s). An introduction to mapping algorithms, as well as factors that influence their results, is provided here.

Moral Duties of Genomics Researchers: Why Personalized Medicine Requires a Collective Approach

Advances in genome sequencing together with the introduction of personalized medicine offer promising new avenues for research and precision treatment, particularly in the field of oncology. At the same time, the convergence of genomics, bioinformatics, and the collection of human tissues and patient data creates novel moral duties for researchers. After all, unprecedented amounts of potentially sensitive information are being generated. Over time, traditional research ethics principles aimed at protecting individual participants have become supplemented with social obligations related to the interests of society and the research enterprise at large, illustrating that genomic medicine is also a social endeavor. In this review we provide a comprehensive assembly of moral duties that have been attributed to genomics researchers and offer suggestions for responsible advancement of personalized genomic cancer care.

Long Single-Molecule Reads Can Resolve the Complexity of the Influenza Virus Composed of Rare, Closely Related Mutant Variants

As a result of a high rate of mutations and recombination events, an RNA-virus exists as a heterogeneous "swarm" of mutant variants. The long read length offered by single-molecule sequencing technologies allows each mutant variant to be sequenced in a single pass. However, high error rate limits the ability to reconstruct heterogeneous viral population composed of rare, related mutant variants. In this article, we present two single-nucleotide variants (2SNV), a method able to tolerate the high error rate of the single-molecule protocol and reconstruct mutant variants. 2SNV uses linkage between single-nucleotide variations to efficiently distinguish them from read errors. To benchmark the sensitivity of 2SNV, we performed a single-molecule sequencing experiment on a sample containing a titrated level of known viral mutant variants. Our method is able to accurately reconstruct clone with frequency of 0.2% and distinguish clones that differed in only two nucleotides distantly located on the genome. 2SNV outperforms existing methods for full-length viral mutant reconstruction.

Clinical Applications of Next-Generation Sequencing in Cancer Diagnosis

With the advancement and improvement of new sequencing technology, next-generation sequencing (NGS) has been applied increasingly in cancer genomics research fields. More recently, NGS has been adopted in clinical oncology to advance personalized treatment of cancer. NGS is utilized to novel diagnostic and rare cancer mutations, detection of translocations, inversions, insertions and deletions, detection of copy number variants, detect familial cancer mutation carriers, provide the molecular rationale for appropriate targeted, therapeutic and prognostic. NGS holds many advantages, such as the ability to fully sequence all types of mutations for a large number of genes (hundreds to thousands) and the sensitivity, speed in a single test at a relatively low cost compared to be other sequencing modalities. Here we described the technology, methods and applications that can be immediately considered and some of the challenges that lie ahead.

Next-Generation Sequencing: The Translational Medicine Approach from "Bench to Bedside to Population"

Humans have predicted the relationship between heredity and diseases for a long time. Only in the beginning of the last century, scientists begin to discover the connotations between different genes and disease phenotypes. Recent trends in next-generation sequencing (NGS) technologies have brought a great momentum in biomedical research that in turn has remarkably augmented our basic understanding of human biology and its associated diseases. State-of-the-art next generation biotechnologies have started making huge strides in our current understanding of mechanisms of various chronic illnesses like cancers, metabolic disorders, neurodegenerative anomalies, etc. We are experiencing a renaissance in biomedical research primarily driven by next generation biotechnologies like genomics, transcriptomics, proteomics, metabolomics, lipidomics etc. Although genomic discoveries are at the forefront of next generation omics technologies, however, their implementation into clinical arena had been painstakingly slow mainly because of high reaction costs and unavailability of requisite computational tools for large-scale data analysis. However rapid innovations and steadily lowering cost of sequence-based chemistries along with the development of advanced bioinformatics tools have lately prompted launching and implementation of large-scale massively parallel genome sequencing programs in different fields ranging from medical genetics, infectious biology, agriculture sciences etc. Recent advances in large-scale omics-technologies is bringing healthcare research beyond the traditional “bench to bedside” approach to more of a continuum that will include improvements, in public healthcare and will be primarily based on predictive, preventive, personalized, and participatory medicine approach (P4). Recent large-scale research projects in genetic and infectious disease biology have indicated that massively parallel whole-genome/whole-exome sequencing, transcriptome analysis, and other functional genomic tools can reveal large number of unique functional elements and/or markers that otherwise would be undetected by traditional sequencing methodologies. Therefore, latest trends in the biomedical research is giving birth to the new branch in medicine commonly referred to as personalized and/or precision medicine. Developments in the post-genomic era are believed to completely restructure the present clinical pattern of disease prevention and treatment as well as methods of diagnosis and prognosis. The next important step in the direction of the precision/personalized medicine approach should be its early adoption in clinics for future medical interventions. Consequently, in coming year’s next generation biotechnologies will reorient medical practice more towards disease prediction and prevention approaches rather than curing them at later stages of their development and progression, even at wider population level(s) for general public healthcare system.

Identification of novel SNPs in differentially expressed genes and its association with horn cancer of Bos indicus bullocks by next-generation sequencing

The use of polymorphic markers like SNPs promises to provide comprehensive tool for analysing genome and identifying genomic regions that contribute to cancer phenotype. Horn cancer is the most common cancer among Bos indicus animals. Increased expression of some genes due to polymorphisms increases risk of HC incidence. We successfully amplified 91 SNPs located in 69 genes in 52 samples, each of HC and HN. Equimolar concentration of amplicons from 69 PCR products of each sample was pooled and subjected to sequencing using Ion Torrent PGM. Data obtained were analysed using DNASTAR software package and case control analysis using SAS software. We found SNP present in BPIFA1 gene of B. indicus shows association with event of HC which reflects its potential to be a genetic marker. Bioinformatic analysis to detect structural and functional impact nsSNP of BPIFA1 added another layer of confirmation to our result. We successfully identified SNP associated with HC as well as demonstrated efficient approach for limited number of SNP discovery and validation in targeted genomics regions in large number of samples combining PCR amplification and Ion Torrent PGM sequencing which suits small-scale laboratories with limited budget.