A glycolytic metabolite bypasses "two-hit" tumor suppression by BRCA2

Knudson's "two-hit" paradigm posits that carcinogenesis requires inactivation of both copies of an autosomal tumor suppressor gene. Here, we report that the glycolytic metabolite methylglyoxal (MGO) transiently bypasses Knudson's paradigm by inactivating the breast cancer suppressor protein BRCA2 to elicit a cancer-associated, mutational single-base substitution (SBS) signature in nonmalignant mammary cells or patient-derived organoids. Germline monoallelic BRCA2 mutations predispose to these changes. An analogous SBS signature, again without biallelic BRCA2 inactivation, accompanies MGO accumulation and DNA damage in Kras-driven, Brca2-mutant murine pancreatic cancers and human breast cancers. MGO triggers BRCA2 proteolysis, temporarily disabling BRCA2's tumor suppressive functions in DNA repair and replication, causing functional haploinsufficiency. Intermittent MGO exposure incites episodic SBS mutations without permanent BRCA2 inactivation. Thus, a metabolic mechanism wherein MGO-induced BRCA2 haploinsufficiency transiently bypasses Knudson's two-hit requirement could link glycolysis activation by oncogenes, metabolic disorders, or dietary challenges to mutational signatures implicated in cancer evolution.

The repertoire of copy number alteration signatures in human cancer

Copy number alterations (CNAs) are a predominant source of genetic alterations in human cancer and play an important role in cancer progression. However comprehensive understanding of the mutational processes and signatures of CNA is still lacking. Here we developed a mechanism-agnostic method to categorize CNA based on various fragment properties, which reflect the consequences of mutagenic processes and can be extracted from different types of data, including whole genome sequencing (WGS) and single nucleotide polymorphism (SNP) array. The 14 signatures of CNA have been extracted from 2778 pan-cancer analysis of whole genomes WGS samples, and further validated with 10 851 the cancer genome atlas SNP array dataset. Novel patterns of CNA have been revealed through this study. The activities of some CNA signatures consistently predict cancer patients' prognosis. This study provides a repertoire for understanding the signatures of CNA in cancer, with potential implications for cancer prognosis, evolution and etiology.

Extrachromosomal DNA (ecDNA): an origin of tumor heterogeneity, genomic remodeling, and drug resistance

The genome of cancer cells contains circular extrachromosomal DNA (ecDNA) elements not found in normal cells. Analysis of clinical samples reveal they are common in most cancers and their presence indicates poor prognosis. They often contain enhancers and driver oncogenes that are highly expressed. The circular ecDNA topology leads to an open chromatin conformation and generates new gene regulatory interactions, including with distal enhancers. The absence of centromeres leads to random distribution of ecDNAs during cell division and genes encoded on them are transmitted in a non-mendelian manner. ecDNA can integrate into and exit from chromosomal DNA. The numbers of specific ecDNAs can change in response to treatment. This dynamic ability to remodel the cancer genome challenges long-standing fundamentals, providing new insights into tumor heterogeneity, cancer genome remodeling, and drug resistance.

Cancer genome and tumor microenvironment: Reciprocal crosstalk shapes lung cancer plasticity

Lung cancer classification and treatment has been revolutionized by improving our understanding of driver mutations and the introduction of tumor microenvironment (TME)-associated immune checkpoint inhibitors. Despite the significant improvement of lung cancer patient survival in response to either oncogene-targeted therapy or anticancer immunotherapy, many patients show initial or acquired resistance to these new therapies. Recent advances in genome sequencing reveal that specific driver mutations favor the development of an immunosuppressive TME phenotype, which may result in unfavorable outcomes in lung cancer patients receiving immunotherapies. Clinical studies with follow-up after immunotherapy, assessing oncogenic driver mutations and the TME immune profile, not only reveal the underlying potential molecular mechanisms in the resistant lung cancer patients but also hold the key to better treatment choices and the future of personalized medicine. In this review, we discuss the crosstalk between cancer cell genomic features and the TME to reveal the impact of genetic alterations on the TME phenotype. We also provide insights into the regulatory role of cellular TME components in defining the genetic landscape of cancer cells during tumor development.

Prospectively defined patterns of APOBEC3A mutagenesis are prevalent in human cancers

Mutational signatures defined by single base substitution (SBS) patterns in cancer have elucidated potential mutagenic processes that contribute to malignancy. Two prevalent mutational patterns in human cancers are attributed to the APOBEC3 cytidine deaminase enzymes. Among the seven human APOBEC3 proteins, APOBEC3A is a potent deaminase and proposed driver of cancer mutagenesis. In this study, we prospectively examine genome-wide aberrations by expressing human APOBEC3A in avian DT40 cells. From whole-genome sequencing, we detect hundreds to thousands of base substitutions per genome. The APOBEC3A signature includes widespread cytidine mutations and a unique insertion-deletion (indel) signature consisting largely of cytidine deletions. This multi-dimensional APOBEC3A signature is prevalent in human cancer genomes. Our data further reveal replication-associated mutations, the rate of stem-loop and clustered mutations, and deamination of methylated cytidines. This comprehensive signature of APOBEC3A mutagenesis is a tool for future studies and a potential biomarker for APOBEC3 activity in cancer.

Systematic illumination of druggable genes in cancer genomes

By combining 6 druggable genome resources, we identify 6,083 genes as potential druggable genes (PDGs). We characterize their expression, recurrent genomic alterations, cancer dependencies, and therapeutic potentials by integrating genome, functionome, and druggome profiles across cancers. 81.5% of PDGs are reliably expressed in major adult cancers, 46.9% show selective expression patterns, and 39.1% exhibit at least one recurrent genomic alteration. We annotate a total of 784 PDGs as dependent genes for cancer cell growth. We further quantify 16 cancer-related features and estimate a PDG cancer drug target score (PCDT score). PDGs with higher PCDT scores are significantly enriched for genes encoding kinases and histone modification enzymes. Importantly, we find that a considerable portion of high PCDT score PDGs are understudied genes, providing unexplored opportunities for drug development in oncology. By integrating the druggable genome and the cancer genome, our study thus generates a comprehensive blueprint of potential druggable genes across cancers.

Jiang et al. generate a comprehensive blueprint of potential druggable genes (PDGs) across cancers by a systematic integration of the druggable genome and the cancer genome. This resource is publicly available to the cancer research community in The Cancer Druggable Gene Atlas (TCDA) through the Functional Cancer Genome data portal.

SCClone: Accurate Clustering of Tumor Single-Cell DNA Sequencing Data

Single-cell DNA sequencing (scDNA-seq) enables high-resolution profiling of genetic diversity among single cells and is especially useful for deciphering the intra-tumor heterogeneity and evolutionary history of tumor. Specific technical issues such as allele dropout, false-positive errors, and doublets make scDNA-seq data incomplete and error-prone, giving rise to a severe challenge of accurately inferring clonal architecture of tumor. To effectively address these issues, we introduce a new computational method called SCClone for reasoning subclones from single nucleotide variation (SNV) data of single cells. Specifically, SCClone leverages a probability mixture model for binary data to cluster single cells into distinct subclones. To accurately decipher underlying clonal composition, a novel model selection scheme based on inter-cluster variance is employed to find the optimal number of subclones. Extensive evaluations on various simulated datasets suggest SCClone has strong robustness against different technical noises in scDNA-seq data and achieves better performance than the state-of-the-art methods in reasoning clonal composition. Further evaluations of SCClone on three real scDNA-seq datasets show that it can effectively find the underlying subclones from severely disturbed data. The SCClone software is freely available at https://github.com/qasimyu/scclone.

Precision Targets for Intercepting the Lethal Progression of Prostate Cancer: Potential Avenues for Personalized Therapy

Organ-confined prostate cancer of low-grade histopathology is managed with radiation, surgery, active surveillance, or watchful waiting and exhibits a 5-year overall survival (OS) of 95%, while metastatic prostate cancer (PCa) is incurable, holding a 5-year OS of 30%. Treatment options for advanced PCa-metastatic and non-metastatic-include hormone therapy that inactivates androgen receptor (AR) signaling, chemotherapy and genome-targeted therapy entailing synthetic lethality of tumor cells exhibiting aberrant DNA damage response, and immune checkpoint inhibition (ICI), which suppresses tumors with genomic microsatellite instability and/or deficient mismatch repair. Cancer genome sequencing uncovered novel somatic and germline mutations, while mechanistic studies are revealing their pathological consequences. A microRNA has shown biomarker potential for stratifying patients who may benefit from angiogenesis inhibition prior to ICI. A 22-gene expression signature may select high-risk localized PCa, which would not additionally benefit from post-radiation hormone therapy. We present an up-to-date review of the molecular and therapeutic aspects of PCa, highlight genomic alterations leading to AR upregulation and discuss AR-degrading molecules as promising anti-AR therapeutics. New biomarkers and druggable targets are shaping innovative intervention strategies against high-risk localized and metastatic PCa, including AR-independent small cell-neuroendocrine carcinoma, while presenting individualized treatment opportunities through improved design and precision targeting.

Primary mucoepidermoid carcinoma of the intrahepatic bile duct: A case report

Mucoepidermoid carcinoma (MEC) is the most common salivary gland carcinoma; however, hepatobiliary MEC is extremely rare. A 74-year-old patient was diagnosed with hepatobiliary MEC after hepatectomy. We considered its origin could be the peribiliary glands. Its genome profile was similar to salivary MEC rather than standard biliary tract carcinoma.

Application of third-generation sequencing in cancer research

In the past several years, nanopore sequencing technology from Oxford Nanopore Technologies (ONT) and single-molecule real-time (SMRT) sequencing technology from Pacific BioSciences (PacBio) have become available to researchers and are currently being tested for cancer research. These methods offer many advantages over most widely used high-throughput short-read sequencing approaches and allow the comprehensive analysis of transcriptomes by identifying full-length splice isoforms and several other posttranscriptional events. In addition, these platforms enable structural variation characterization at a previously unparalleled resolution and direct detection of epigenetic marks in native DNA and RNA. Here, we present a comprehensive summary of important applications of these technologies in cancer research, including the identification of complex structure variants, alternatively spliced isoforms, fusion transcript events, and exogenous RNA. Furthermore, we discuss the impact of the newly developed nanopore direct RNA sequencing (RNA-Seq) approach in advancing epitranscriptome research in cancer. Although the unique challenges still present for these new single-molecule long-read methods, they will unravel many aspects of cancer genome complexity in unprecedented ways and present an encouraging outlook for continued application in an increasing number of different cancer research settings.

Gene Duplication and Gene Fusion Are Important Drivers of Tumourigenesis during Cancer Evolution

Chromosomal rearrangement and genome instability are common features of cancer cells in human. Consequently, gene duplication and gene fusion events are frequently observed in human malignancies and many of the products of these events are pathogenic, representing significant drivers of tumourigenesis and cancer evolution. In certain subsets of cancers duplicated and fused genes appear to be essential for initiation of tumour formation, and some even have the capability of transforming normal cells, highlighting the importance of understanding the events that result in their formation. The mechanisms that drive gene duplication and fusion are unregulated in cancer and they facilitate rapid evolution by selective forces akin to Darwinian survival of the fittest on a cellular level. In this review, we examine current knowledge of the landscape and prevalence of gene duplication and gene fusion in human cancers.

Mutations and Copy Number Abnormalities of Hippo Pathway Components in Human Cancers

The Hippo pathway is highly conserved from Drosophila to mammals. As a key regulator of cell proliferation, the Hippo pathway controls tissue homeostasis and has a major impact on tumorigenesis. The originally defined core components of the Hippo pathway in mammals include STK3/4, LATS1/2, YAP1/TAZ, TEAD, VGLL4, and NF2. However, for most of these genes, mutations and copy number variations are relatively uncommon in human cancer. Several other recently identified upstream and downstream regulators of Hippo signaling, including FAT1, SHANK2, Gq/11, and SWI/SNF complex, are more commonly dysregulated in human cancer at the genomic level. This review will discuss major genomic events in human cancer that enable cancer cells to escape the tumor-suppressive effects of Hippo signaling.

The Cancer Genome: Paradigm or Paradox?

Simple Summary

The observation that genetic mutations often do not cause cancer or disease in the phenomena of mosaicism, clonal hematopoiesis of indeterminate potential (CHIP), and heteroplasmy provides us with important clues about the origin and nature of cancer. We should be wary that the cancer genome may lead us astray to the wrong destination on a bad expedition unless we adopt the right cancer theory to elucidate it, and adhere to the proper scientific method to investigate it.

Abstract

Nowadays, many professionals are sequencing the DNA and studying the cancer genome. However, if the genetic theory of cancer is flawed, our faith in the cancer genome will falter. If gene sequencing is only a tool, we should question what we are making or creating with this tool. When we do not have the right cancer theory at our disposal, we cannot be sure that what we create from the cancer genome is meaningful or useful. In this article, we illustrate that mosaicism, CHIP, and heteroplasmy dispute our traditional perspectives about a genetic origin of cancer and challenge our current narratives about the cancer genome. We caution that when we have the wrong cancer theory, big data can provide poor evidence. Precision medicine may become rather imprecise. Targeted therapy either does not work or work for the wrong reasons. The cancer genome thus becomes a paradox rather than a paradigm.

Parallelized Latent Dirichlet Allocation Provides a Novel Interpretability of Mutation Signatures in Cancer Genomes

Mutation signatures are defined as the distribution of specific mutations such as activity of AID/APOBEC family proteins. Previous studies have reported numerous signatures, using matrix factorization methods for mutation catalogs. Different mutation signatures are active in different tumor types; hence, signature activity varies greatly among tumor types and becomes sparse. Because of this, many previous methods require dividing mutation catalogs for each tumor type. Here, we propose parallelized latent Dirichlet allocation (PLDA), a novel Bayesian model to simultaneously predict mutation signatures with all mutation catalogs. PLDA is an extended model of latent Dirichlet allocation (LDA), which is one of the methods used for signature prediction. It has parallelized hyperparameters of Dirichlet distributions for LDA, and they represent the sparsity of signature activities for each tumor type, thus facilitating simultaneous analyses. First, we conducted a simulation experiment to compare PLDA with previous methods (including SigProfiler and SignatureAnalyzer) using artificial data and confirmed that PLDA could predict signature structures as accurately as previous methods without searching for the optimal hyperparameters. Next, we applied PLDA to PCAWG (Pan-Cancer Analysis of Whole Genomes) mutation catalogs and obtained a signature set different from the one predicted by SigProfiler. Further, we have shown that the mutation spectrum represented by the predicted signature with PLDA provides a novel interpretability through post-analyses.

Identifying Epistasis in Cancer Genomes: A Delicate Affair

Recent studies of the tumor genome seek to identify cancer pathways as groups of genes in which mutations are epistatic with one another or, specifically, "mutually exclusive." Here, we show that most mutations are mutually exclusive not due to pathway structure but to interactions with disease subtype and tumor mutation load. In particular, many cancer driver genes are mutated preferentially in tumors with few mutations overall, causing mutations in these cancer genes to appear mutually exclusive with numerous others. Researchers should view current epistasis maps with caution until we better understand the multiple cause-and-effect relationships among factors such as tumor subtype, positive selection for mutations, and gross tumor characteristics including mutational signatures and load.

MVSC: A Multi-variation Simulator of Cancer Genome

BACKGROUND

Many forms of variations exist in the genome, which are the main causes of individual phenotypic differences. The detection of variants, especially those located in the tumor genome, still faces many challenges due to the complexity of the genome structure. Thus, the performance assessment of variation detection tools using next-generation sequencing platforms is urgently needed.

METHOD

We have created a software package called the Multi-Variation Simulator of Cancer genomes (MVSC) to simulate common genomic variants, including single nucleotide polymorphisms, small insertion and deletion polymorphisms, and structural variations (SVs), which are analogous to human somatically acquired variations. Three sets of variations embedded in genomic sequences in different periods were dynamically and sequentially simulated one by one.

RESULTS

In cancer genome simulation, complex SVs are important because this type of variation is characteristic of the tumor genome structure. Overlapping variations of different sizes can also coexist in the same genome regions, adding to the complexity of cancer genome architecture. Our results show that MVSC can efficiently simulate a variety of genomic variants that cannot be simulated by existing software packages.

CONCLUSION

The MVSC-simulated variants can be used to assess the performance of existing tools designed to detect SVs in next-generation sequencing data, and we also find that MVSC is memory and time-efficient compared with similar software packages.

Japanese version of The Cancer Genome Atlas, JCGA, established using fresh frozen tumors obtained from 5143 cancer patients

This study aimed to establish the Japanese Cancer Genome Atlas (JCGA) using data from fresh frozen tumor tissues obtained from 5143 Japanese cancer patients, including those with colorectal cancer (31.6%), lung cancer (16.5%), gastric cancer (10.8%) and other cancers (41.1%). The results are part of a single-center study called "High-tech Omics-based Patient Evaluation" or "Project HOPE" conducted at the Shizuoka Cancer Center, Japan. All DNA samples and most RNA samples were analyzed using whole-exome sequencing, cancer gene panel sequencing, fusion gene panel sequencing and microarray gene expression profiling, and the results were annotated using an analysis pipeline termed "Shizuoka Multi-omics Analysis Protocol" developed in-house. Somatic driver alterations were identified in 72.2% of samples in 362 genes (average, 2.3 driver events per sample). Actionable information on drugs that is applicable in the current clinical setting was associated with 11.3% of samples. When including those drugs that are used for investigative purposes, actionable information was assigned to 55.0% of samples. Germline analysis revealed pathogenic mutations in hereditary cancer genes in 9.2% of samples, among which 12.2% were confirmed as pathogenic mutations by confirmatory test. Pathogenic mutations associated with non-cancerous hereditary diseases were detected in 0.4% of samples. Tumor mutation burden (TMB) analysis revealed 5.4% of samples as having the hypermutator phenotype (TMB ≥ 20). Clonal hematopoiesis was observed in 8.4% of samples. Thus, the JCGA dataset and the analytical procedures constitute a fundamental resource for genomic medicine for Japanese cancer patients.

Synthetic Lethality in Lung Cancer-From the Perspective of Cancer Genomics

Cancer is a genetic disease, and this concept is now widely exploited by both scientists and clinicians to develop new genotype-selective anticancer therapeutics. Although the quest of cancer genomics is in its dawn, recognition of the widespread applicability of genetic interactions with biological processes of tumorigenesis is propelling research throughout academic fields. Lung cancer is the most common cause of cancer death worldwide, with an estimated 1.6 million deaths each year. Despite the development of targeted therapies that inhibit oncogenic mutations of lung cancer cases, continued research into new therapeutic approaches is required for untreatable lung cancer patients, and the development of therapeutic modalities has proven elusive. The "synthetic lethal" approach holds the promise of delivering a therapeutic regimen that preferentially targets malignant cells while sparing normal cells. We highlight the potential challenges in synthetic lethal anticancer therapeutics that target untreatable genetic alterations in lung cancer. We also discuss both challenges and opportunities regarding the application of new synthetic lethal interactions in lung cancer.

Phosphatidylinositol 3-kinase pathway genomic alterations in 60,991 diverse solid tumors informs targeted therapy opportunities

Background

The phosphatidylinositol 3‐kinase (PI3K) pathway is frequently altered in cancer. This report describes the landscape of PI3K alterations in solid tumors as well as co‐alterations serving as potential resistance/attenuation mechanisms.

Methods

Consecutive samples were analyzed in a commercial Clinical Laboratory Improvement Amendment‐certified laboratory using comprehensive genomic profiling performed by next‐generation sequencing (315 genes). The co‐alterations evaluated included the Erb‐B2 receptor tyrosine kinase 2 (ERBB2), ERBB3, ERBB4, RAS, MET proto‐oncogene tyrosine kinase (MET), and mitogen‐activated protein kinase kinase (MAP2K) genes as well as tumor protein 53 (TP53), estrogen receptor 1 (ESR1), and androgen receptor (AR).

Results

Alterations in any of 18 PI3K‐pathway associated genes were identified in 44% of 60,991 tumors. Although single base and insertions/deletions (indels) were the most frequent alterations, copy number changes and rearrangements were identified in 11% and 0.9% of patients, respectively. Overall, the most frequently altered genes were PIK3 catalytic subunit α (PIK3CA) (13%), phosphatase and tensin homolog (PTEN) (9%), and serine/threonine kinase 11 (STK11) (5%). Tumor types that frequently harbored at least 1 PI3K alteration were uterine (77%), cervical (62%), anal (59%), and breast (58%) cancers. Alterations also were discerned frequently in tumors with carcinosarcoma (89%) and squamous cell carcinoma (62%) histologies. Tumors with a greater likelihood of co‐occurring PI3K pathway and MAPK pathway alterations included colorectal cancers (odds ratio [OR], 1.64; P < .001), mesotheliomas (OR, 2.67; P = .024), anal cancers (OR, 1.98; P = .03), and nonsquamous head and neck cancers (OR, 2.03; P = .019). The co‐occurrence of ESR1 and/or AR alterations with PI3K alterations was statistically significant in bladder, colorectal, uterine, prostate, and unknown primary cancers.

Conclusions

Comprehensive genomic profiling reveals altered PI3K‐related genes in 44% of solid malignancies, including rare disease and histology types. The frequency of alterations and the co‐occurrence of resistance pathways vary by tumor type, directly affecting opportunities for targeted therapy.

Comprehensive genomic profiling of solid tumors reveals frequent genetic alterations in several genes of the phosphatidylinositol 3‐kinase (PI3K) pathway. Data from this analysis suggest that in‐depth characterization of the PI3K pathway along with concomitant resistance alterations in other pathways can provide a genomic background for the development of future treatments.

Copy Number Alterations in Tumor Genomes Deleting Antineoplastic Drug Targets Partially Compensated by Complementary Amplifications

BACKGROUND/AIM

Genomic DNA copy number alterations (CNAs) are frequent in tumors and have been catalogued by The Cancer Genome Atlas project. Emergence of chemoresistance frequently renders drug therapies ineffective.

MATERIALS AND METHODS

We analyzed how CNAs recurrently found in the genomes of TCGA patients of thirty-one tumor types affect protein targets of antineoplastic (AN) agents.

RESULTS

CNA deletions more frequently affected the targets of AN agents than CNA amplifications. Interestingly, in seven tumors we observed signs of compensatory CNAs. For example, in glioblastoma multiforme, two target genes (FLT1, FLT3) of the experimental drug sorafenib were recurrently deleted, whereas another target (KDR) of sorafenib was recurrently amplified. In renal clear cell carcinoma, the target FLT1 of pazopanib, sunitinib, sorafenib, and axitinib was recurrently deleted, whereas FLT4 bound by the same drugs, was recurrently amplified.

CONCLUSION

Deletions of AN target proteins can be compensated by amplification of alternative targets.

[Searching for essential genes in cancer genomes]

The concept of essential genes, whose loss of functionality leads to cell death, is one of the fundamental concepts of genetics and is important for fundamental and applied research. This field is particularly promising in relation to oncology, since the search for genetic vulnerabilities of cancer cells allows us to identify new potential targets for antitumor therapy. The modern biotechnology capacities allow carrying out large-scale projects for sequencing somatic mutations in tumors, as well as directly interfering the genetic apparatus of cancer cells. They provided accumulation of a considerable body of knowledge about genetic variants and corresponding phenotypic manifestations in tumors. In the near future this knowledge will find application in clinical practice. This review describes the main experimental and computational approaches to the search for essential genes, concentrating on the application of these methods in the field of molecular oncology.

Proteogenomics of Malignant Melanoma Cell Lines: The Effect of Stringency of Exome Data Filtering on Variant Peptide Identification in Shotgun Proteomics

The identification of genetically encoded variants at the proteome level is an important problem in cancer proteogenomics. The generation of customized protein databases from DNA or RNA sequencing data is a crucial stage of the identification workflow. Genomic data filtering applied at this stage may significantly modify variant search results, yet its effect is generally left out of the scope of proteogenomic studies. In this work, we focused on this impact using data of exome sequencing and LC-MS/MS analyses of six replicates for eight melanoma cell lines processed by a proteogenomics workflow. The main objectives were identifying variant peptides and revealing the role of the genomic data filtering in the variant identification. A series of six confidence thresholds for single nucleotide polymorphisms and indels from the exome data were applied to generate customized sequence databases of different stringency. In the searches against unfiltered databases, between 100 and 160 variant peptides were identified for each of the cell lines using X!Tandem and MS-GF+ search engines. The recovery rate for variant peptides was ∼1%, which is approximately three times lower than that of the wild-type peptides. Using unfiltered genomic databases for variant searches resulted in higher sensitivity and selectivity of the proteogenomic workflow and positively affected the ability to distinguish the cell lines based on variant peptide signatures.

Whole genome sequencing analysis for cancer genomics and precision medicine

Explosive advances in next-generation sequencer (NGS) and computational analyses have enabled exploration of somatic protein-altered mutations in most cancer types, with coding mutation data intensively accumulated. However, there is limited information on somatic mutations in non-coding regions, including introns, regulatory elements and non-coding RNA. Structural variants and pathogen in cancer genomes remain widely unexplored. Whole genome sequencing (WGS) approaches can be used to comprehensively explore all types of genomic alterations in cancer and help us to better understand the whole landscape of driver mutations and mutational signatures in cancer genomes and elucidate the functional or clinical implications of these unexplored genomic regions and mutational signatures. This review describes recently developed technical approaches for cancer WGS and the future direction of cancer WGS, and discusses its utility and limitations as an analysis platform and for mutation interpretation for cancer genomics and cancer precision medicine. Taking into account the diversity of cancer genomes and phenotypes, interpretation of abundant mutation information from WGS, especially non-coding and structure variants, requires the analysis of large-scale WGS data integrated with RNA-Seq, epigenomics, immuno-genomic and clinic-pathological information.

Clonal evolution of acute myeloid leukemia highlighted by latest genome sequencing studies

Decades of years might be required for an initiated cell to become a fully-pledged, metastasized tumor. DNA mutations are accumulated during this process including background mutations that emerge scholastically, as well as driver mutations that selectively occur in a handful of cancer genes and confer the cell a growth advantage over its neighbors. A clone of tumor cells could be superseded by another clone that acquires new mutations and grows more aggressively. Tumor evolutional patterns have been studied for years using conventional approaches that focus on the investigation of a single or a couple of genes. Latest deep sequencing technology enables a global view of tumor evolution by deciphering almost all genome aberrations in a tumor. Tumor clones and the fate of each clone during tumor evolution can be depicted with the help of the concept of variant allele frequency. Here, we summarize the new insights of cancer evolutional progression in acute myeloid leukemia.

Genomic Hallmarks of Thyroid Neoplasia

The genomic landscape of thyroid cancers that are derived from follicular cells has been substantially elucidated through the coordinated application of high-throughput genomic technologies. Here, I review the common genetic alterations across the spectrum of thyroid neoplasia and present the resulting model of thyroid cancer initiation and progression. This model illustrates the striking correlation between tumor differentiation and overall somatic mutational burden, which also likely explains the highly variable clinical behavior and outcome of patients with thyroid cancers. These advances are yielding critical insights into thyroid cancer pathogenesis, which are being leveraged for the development of new diagnostic tools, prognostic and predictive biomarkers, and novel therapeutic approaches.

Primary liver cancer genome sequencing: translational implications and challenges

The prognosis of primary liver cancer (PLC) remains poor and is explained by the slow progress in understanding the molecular pathways driving tumorigenesis, therapeutic resistance and relapse. For early PLCs, complete surgical resection is the only effective treatment, with sorafenib and, more recently, regorafenib prolonging overall survival by a few months. Areas covered: Application of next-generation sequencing (NGS), including targeted NGS (tNGS), whole-exome sequencing (WES), whole-genome sequencing (WGS) and RNA sequencing (RNAseq), on clinical samples from patients with hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) could aid in comprehending tumorigenesis, genetic and genomic heterogeneity, as well as developing molecular classifications for specialized targeted therapy. Expert commentary: Despite the many overenthusiastic original and opinion reports, we have critically reviewed available NGS studies, with focus on the challenges to achieve clinical implications. Based on the recommendations for valid identification of clinically crucial genomic alterations (GAs) by NGS, we propose NGS integration into appropriately designed clinical trials. Furthermore, valid detection of genomic heterogeneity enables the conduction of clinical trials investigating the efficacy both of GAs as prognostic and predictive tools, as well as the discovery of novel oncotargets, on the basis of an early drug development strategy.

GeneBreak: detection of recurrent DNA copy number aberration-associated chromosomal breakpoints within genes

Development of cancer is driven by somatic alterations, including numerical and structural chromosomal aberrations. Currently, several computational methods are available and are widely applied to detect numerical copy number aberrations (CNAs) of chromosomal segments in tumor genomes. However, there is lack of computational methods that systematically detect structural chromosomal aberrations by virtue of the genomic location of CNA-associated chromosomal breaks and identify genes that appear non-randomly affected by chromosomal breakpoints across (large) series of tumor samples. ‘GeneBreak’ is developed to systematically identify genes recurrently affected by the genomic location of chromosomal CNA-associated breaks by a genome-wide approach, which can be applied to DNA copy number data obtained by array-Comparative Genomic Hybridization (CGH) or by (low-pass) whole genome sequencing (WGS). First, ‘GeneBreak’ collects the genomic locations of chromosomal CNA-associated breaks that were previously pinpointed by the segmentation algorithm that was applied to obtain CNA profiles. Next, a tailored annotation approach for breakpoint-to-gene mapping is implemented. Finally, dedicated cohort-based statistics is incorporated with correction for covariates that influence the probability to be a breakpoint gene. In addition, multiple testing correction is integrated to reveal recurrent breakpoint events. This easy-to-use algorithm, ‘GeneBreak’, is implemented in R ( www.cran.r-project.org) and is available from Bioconductor ( www.bioconductor.org/packages/release/bioc/html/GeneBreak.html).

Synthetic essentiality: Targeting tumor suppressor deficiencies in cancer

In this review, we summarize recent work exploring a novel conceptual approach termed "synthetic essentiality" as a means for targeting specific tumor suppressor gene deficiencies in cancer. With the aid of extensive publically available cancer genome and clinical databases, "synthetic essentiality" could be utilized to identify synthetic essential genes, which might be occasionally deleted in some cancers but almost always retained in the context of a specific tumor suppressor deficiency. Synthetic essentiality expands the existing concepts for therapeutic strategies, including oncogene addiction, tumor maintenance, synthetic, and collateral lethality, to provide a framework for the discovery of cancer-specific vulnerabilities. Enabled by ever-expanding large-scale genome datasets and genome-scale functional screens, the "synthetic essentiality" framework provides an avenue for the identification of context-specific therapeutic targets and development of patient responder hypotheses for novel and existing therapies.

OncoBinder facilitates interpretation of proteomic interaction data by capturing coactivation pairs in cancer

High-throughput methods such as co-immunoprecipitationmass spectrometry (coIP-MS) and yeast 2 hybridization (Y2H) have suggested a broad range of unannotated protein-protein interactions (PPIs), and interpretation of these PPIs remains a challenging task. The advancements in cancer genomic researches allow for the inference of "coactivation pairs" in cancer, which may facilitate the identification of PPIs involved in cancer. Here we present OncoBinder as a tool for the assessment of proteomic interaction data based on the functional synergy of oncoproteins in cancer. This decision tree-based method combines gene mutation, copy number and mRNA expression information to infer the functional status of protein-coding genes. We applied OncoBinder to evaluate the potential binders of EGFR and ERK2 proteins based on the gastric cancer dataset of The Cancer Genome Atlas (TCGA). As a result, OncoBinder identified high confidence interactions (annotated by Kyoto Encyclopedia of Genes and Genomes (KEGG) or validated by low-throughput assays) more efficiently than co-expression based method. Taken together, our results suggest that evaluation of gene functional synergy in cancer may facilitate the interpretation of proteomic interaction data. The OncoBinder toolbox for Matlab is freely accessible online.

Clarifying the biological significance of the CHK2 K373E somatic mutation discovered in The Cancer Genome Atlas database

We identified CHK2 K373E as a recurrent mutation in The Cancer Genome Atlas (TCGA) database. In this study, we demonstrate that the K373E mutation disrupts CHK2 autophosphorylation as well as kinase activity, thus leading to impairment of CHK2 functions in suppressing cell proliferation and promoting cell survival after ionizing radiation. We propose that K373E impairs p53-independent induction of p21^WAF1/CIP1 by CHK2. Our data implicate the K373E mutation of CHK2 in tumorigenesis.

Using Pharmacogenomic Databases for Discovering Patient-Target Genes and Small Molecule Candidates to Cancer Therapy

With multiple omics strategies being applied to several cancer genomics projects, researchers have the opportunity to develop a rational planning of targeted cancer therapy. The investigation of such numerous and diverse pharmacogenomic datasets is a complex task. It requires biological knowledge and skills on a set of tools to accurately predict signaling network and clinical outcomes. Herein, we describe Web-based in silico approaches user friendly for exploring integrative studies on cancer biology and pharmacogenomics. We briefly explain how to submit a query to cancer genome databases to predict which genes are significantly altered across several types of cancers using CBioPortal. Moreover, we describe how to identify clinically available drugs and potential small molecules for gene targeting using CellMiner. We also show how to generate a gene signature and compare gene expression profiles to investigate the complex biology behind drug response using Connectivity Map. Furthermore, we discuss on-going challenges, limitations and new directions to integrate molecular, biological and epidemiological information from oncogenomics platforms to create hypothesis-driven projects. Finally, we discuss the use of Patient-Derived Xenografts models (PDXs) for drug profiling in vivo assay. These platforms and approaches are a rational way to predict patient-targeted therapy response and to develop clinically relevant small molecules drugs.

Targeted Cancer Therapy: Vital Oncogenes and a New Molecular Genetic Paradigm for Cancer Initiation Progression and Treatment

It has been declared repeatedly that cancer is a result of molecular genetic abnormalities. However, there has been no working model describing the specific functional consequences of the deranged genomic processes that result in the initiation and propagation of the cancer process during carcinogenesis. We no longer need to question whether or not cancer arises as a result of a molecular genetic defect within the cancer cell. The legitimate questions are: how and why? This article reviews the preeminent data on cancer molecular genetics and subsequently proposes that the sentinel event in cancer initiation is the aberrant production of fused transcription activators with new molecular properties within normal tissue stem cells. This results in the production of vital oncogenes with dysfunctional gene activation transcription properties, which leads to dysfunctional gene regulation, the aberrant activation of transduction pathways, chromosomal breakage, activation of driver oncogenes, reactivation of stem cell transduction pathways and the activation of genes that result in the hallmarks of cancer. Furthermore, a novel holistic molecular genetic model of cancer initiation and progression is presented along with a new paradigm for the approach to personalized targeted cancer therapy, clinical monitoring and cancer diagnosis.

Whole-genome sequencing of nine esophageal adenocarcinoma cell lines

Esophageal adenocarcinoma (EAC) is highly mutated and molecularly heterogeneous. The number of cell lines available for study is limited and their genome has been only partially characterized. The availability of an accurate annotation of their mutational landscape is crucial for accurate experimental design and correct interpretation of genotype-phenotype findings. We performed high coverage, paired end whole genome sequencing on eight EAC cell lines-ESO26, ESO51, FLO-1, JH-EsoAd1, OACM5.1 C, OACP4 C, OE33, SK-GT-4-all verified against original patient material, and one esophageal high grade dysplasia cell line, CP-D. We have made available the aligned sequence data and report single nucleotide variants (SNVs), small insertions and deletions (indels), and copy number alterations, identified by comparison with the human reference genome and known single nucleotide polymorphisms (SNPs). We compare these putative mutations to mutations found in primary tissue EAC samples, to inform the use of these cell lines as a model of EAC.

A network-based drug repositioning infrastructure for precision cancer medicine through targeting significantly mutated genes in the human cancer genomes

OBJECTIVE

Development of computational approaches and tools to effectively integrate multidomain data is urgently needed for the development of newly targeted cancer therapeutics.

METHODS

We proposed an integrative network-based infrastructure to identify new druggable targets and anticancer indications for existing drugs through targeting significantly mutated genes (SMGs) discovered in the human cancer genomes. The underlying assumption is that a drug would have a high potential for anticancer indication if its up-/down-regulated genes from the Connectivity Map tended to be SMGs or their neighbors in the human protein interaction network.

RESULTS

We assembled and curated 693 SMGs in 29 cancer types and found 121 proteins currently targeted by known anticancer or noncancer (repurposed) drugs. We found that the approved or experimental cancer drugs could potentially target these SMGs in 33.3% of the mutated cancer samples, and this number increased to 68.0% by drug repositioning through surveying exome-sequencing data in approximately 5000 normal-tumor pairs from The Cancer Genome Atlas. Furthermore, we identified 284 potential new indications connecting 28 cancer types and 48 existing drugs (adjusted P < .05), with a 66.7% success rate validated by literature data. Several existing drugs (e.g., niclosamide, valproic acid, captopril, and resveratrol) were predicted to have potential indications for multiple cancer types. Finally, we used integrative analysis to showcase a potential mechanism-of-action for resveratrol in breast and lung cancer treatment whereby it targets several SMGs (ARNTL, ASPM, CTTN, EIF4G1, FOXP1, and STIP1).

CONCLUSIONS

In summary, we demonstrated that our integrative network-based infrastructure is a promising strategy to identify potential druggable targets and uncover new indications for existing drugs to speed up molecularly targeted cancer therapeutics.

Understanding the Genetic Mechanisms of Cancer Drug Resistance Using Genomic Approaches

A major obstacle in precision cancer medicine is the inevitable resistance to targeted therapies. Tremendous effort and progress has been made over the past few years to understand the biochemical and genetic mechanisms underlying drug resistance, with the goal to eventually overcome such daunting challenges. Diverse mechanisms, such as secondary mutations, oncogene bypass, and epigenetic alterations, can all lead to drug resistance, and the number of known involved genes is growing rapidly, thus providing many possibilities to overcome resistance. The finding of these mechanisms and genes invariably requires the application of genomic and functional genomic approaches to tumors or cancer models. In this review, we briefly highlight the major drug-resistance mechanisms known today, and then focus primarily on the technological approaches leading to the advancement of this field.

Targeted therapies for ER+/HER2- metastatic breast cancer

The majority of breast cancers present with estrogen receptor (ER)-positive and human epidermal growth factor receptor (HER2)-negative features and might benefit from endocrine therapy. Although endocrine therapy has notably evolved during the last decades, the invariable appearance of endocrine resistance, either primary or secondary, remains an important issue in this type of tumor. The improvement of our understanding of the cancer genome has identified some promising targets that might be responsible or linked to endocrine resistance, including alterations affecting main signaling pathways like PI3K/Akt/mTOR and CCND1/CDK4-6 as well as the identification of new ESR1 somatic mutations, leading to an array of new targeted therapies that might circumvent or prevent endocrine resistance. In this review, we have summarized the main targeted therapies that are currently being tested in ER+ breast cancer, the rationale behind them, and the new agents and combinational treatments to come.

Somatic structural variation and cancer

With the advent of next-generation sequencing technologies, we have witnessed a rapid pace of discovery of new patterns of somatic structural variation in cancer genomes, and an attempt to figure out their underlying mechanisms. Some of these mechanisms are associated with particular cancer types, and in some cases are the main cause of the structural mutations that drive the oncogenic process. This review provides an overview of the patterns of somatic structural variation and chromosomal structures that characterize cancer genomes, their causal mechanisms and their impact in oncogenesis.

Somatic DNA mutation analysis in targeted therapy of solid tumours

Cancer is a disease of the genome with diverse aetiologies including the accumulation of acquired mutations throughout the genome. There has been a flood of knowledge improving our understanding of the biology and molecular genetics of melanoma, lung and colorectal cancer since the genomics era started. Translation of this knowledge into a better understanding of cell proliferation, survival and apoptosis has produced a paradigm shift in medical oncology enabling gene-based cancer treatment (called personalised or precision medicine). Somatic mutation analysis is crucial for a genomics approach since it can identify driver mutations-the "Achilles' heel" of cancer, and support clinical decision-making through targeted therapy. Nevertheless, the applications of somatic DNA testing in cancer face many challenges such as obtaining comprehensive coverage of the cancer genome with limited DNA being available, and delivering an accurate report in a timely fashion without false-negative and false-positive results. Further advances in DNA technologies and bioinformatics will overcome these issues and maximise opportunities for targeted therapy. Somatic mutation analysis will then become an integral part of cancer management for all malignancies.

Origins and functional consequences of somatic mitochondrial DNA mutations in human cancer

Recent sequencing studies have extensively explored the somatic alterations present in the nuclear genomes of cancers. Although mitochondria control energy metabolism and apoptosis, the origins and impact of cancer-associated mutations in mtDNA are unclear. In this study, we analyzed somatic alterations in mtDNA from 1675 tumors. We identified 1907 somatic substitutions, which exhibited dramatic replicative strand bias, predominantly C > T and A > G on the mitochondrial heavy strand. This strand-asymmetric signature differs from those found in nuclear cancer genomes but matches the inferred germline process shaping primate mtDNA sequence content. A number of mtDNA mutations showed considerable heterogeneity across tumor types. Missense mutations were selectively neutral and often gradually drifted towards homoplasmy over time. In contrast, mutations resulting in protein truncation undergo negative selection and were almost exclusively heteroplasmic. Our findings indicate that the endogenous mutational mechanism has far greater impact than any other external mutagens in mitochondria and is fundamentally linked to mtDNA replication.

Regional biases in mutation screening due to intratumoural heterogeneity of prostate cancer

Intratumoural heterogeneity (ITH) leads to regional biases of the mutational landscape in a single tumour and may influence the single biopsy-based clinical diagnosis and treatment decision. To evaluate the extent of ITH in unifocal prostate cancers (PCAs), we analysed multiple regional biopsies from three PCAs, using whole-exome sequencing, DNA copy number and gene expression profiling analyses. A substantial level of ITH was identified, in that 0-61% and 18-71% of somatic variants were common or private, respectively, within a given cancer. The enhanced mutation detection rate in the combined sequencing dataset across intratumoural biopsies was demonstrated with respect to the total number of mutations identified in a given tumour. Allele frequencies of the mutations were positively correlated with the levels of intratumoural recurrence (private < shared < common), but some common mutations showed low allele frequency, suggesting that not all were clonally fixed. Regional biases in the presentation of a well-known TMPRSS2-ERG fusion was noted in one PCA and the somatic mutation- and copy number-based phylogenetic relationships between intratumoural biopsies were largely concordant. Genes showing intratumoural expression variability were commonly enriched in the molecular function of eicosanoid metabolism and PCA-relevant clinical markers. Taken together, our analyses identified a substantial level of genetic ITH in unifocal PCAs at the mutation, copy number and expression levels, which should be taken into account for the identification of biomarkers in the clinical setting.

Clinical implications of next-generation sequencing for cancer medicine

In 1976, Peter Nowell, following observations of cytogenetic heterogeneity in a population of cancer-ous cells, proposed that this genetic diversity could be explained by hypothesizing that these cells are subject to evolutionary forces[...]

Mutational heterogeneity in human cancers: origin and consequences

Cancer recapitulates Darwinian evolution. Mutations acquired during life that provide cells with a growth or survival advantage will preferentially multiply to form a tumor. As a result of The Cancer Genome Atlas Project, we have gathered detailed information on the nucleotide sequence changes in a number of human cancers. The sources of mutations in cancer are diverse, and the complexity of those found to be clonally present in tumors has increasingly made it difficult to identify key rate-limiting genes for tumor growth that could serve as potential targets for directed therapies. The impact of DNA sequencing on future cancer research and personalized therapy is likely to be profound and merits critical evaluation.

Computational methods for the analysis of array comparative genomic hybridization

Array comparative genomic hybridization (array CGH) is a technique for assaying the copy number status of cancer genomes. The widespread use of this technology has lead to a rapid accumulation of high throughput data, which in turn has prompted the development of computational strategies for the analysis of array CGH data. Here we explain the principles behind array image processing, data visualization and genomic profile analysis, review currently available software packages, and raise considerations for future software development.