Deep sequencing techniques provide a remarkable opportunity for comprehensive understanding of tumorigenesis at the molecular level. As omics studies become popular, integrative approaches need to be developed to move from a simple cataloguing of mutations and changes in gene expression to dissecting the molecular nature of carcinogenesis at the systemic level and understanding the complex networks that lead to cancer development.
Here, we describe a high-throughput, multi-dimensional sequencing study of primary lung adenocarcinoma tumors and adjacent normal tissues of six Korean female never-smoker patients. Our data encompass results from exome-seq, RNA-seq, small RNA-seq, and MeDIP-seq. We identified and validated novel genetic aberrations, including 47 somatic mutations and 19 fusion transcripts. One of the fusions involves the c-RET gene, which was recently reported to form fusion genes that may function as drivers of carcinogenesis in lung cancer patients. We also characterized gene expression profiles, which we integrated with genomic aberrations and gene regulations into functional networks. The most prominent gene network module that emerged indicates that disturbances in G2/M transition and mitotic progression are causally linked to tumorigenesis in these patients. Also, results from the analysis strongly suggest that several novel microRNA-target interactions represent key regulatory elements of the gene network.
Our study not only provides an overview of the alterations occurring in lung adenocarcinoma at multiple levels from genome to transcriptome and epigenome, but also offers a model for integrative genomics analysis and proposes potential target pathways for the control of lung adenocarcinoma.
The research community at large is expending considerable resources to sequence the coding region of the genomes of tumors and other human diseases using targeted exome capture (i.e., “whole exome sequencing”). The primary goal of targeted exome sequencing is to identify nonsynonymous mutations that potentially have functional consequences. Here, we demonstrate that whole-exome sequencing data can also be analyzed for comprehensively monitoring somatic copy number alterations (CNAs) by benchmarking the technique against conventional array CGH. A series of 17 matched tumor and normal tissues from patients with metastatic castrate-resistant prostate cancer was used for this assessment. We show that targeted exome sequencing reliably identifies CNAs that are common in advanced prostate cancer, such as androgen receptor (AR) gain and PTEN loss. Taken together, these data suggest that targeted exome sequencing data can be effectively leveraged for the detection of somatic CNAs in cancer.
Recent advances in the treatment of cancer have focused on targeting genomic aberrations with selective therapeutic agents. In rare tumors, where large-scale clinical trials are daunting, this targeted genomic approach offers a new perspective and hope for improved treatments. Cancers of the ampulla of Vater are rare tumors that comprise only about 0.2% of gastrointestinal cancers. Consequently, they are often treated as either distal common bile duct or pancreatic cancers.
We analyzed DNA from a resected cancer of the ampulla of Vater and whole blood DNA from a 63 year-old man who underwent a pancreaticoduodenectomy by whole genome sequencing, achieving 37× and 40× coverage, respectively. We determined somatic mutations and structural alterations.
We identified relevant aberrations, including deleterious mutations of KRAS and SMAD4 as well as a homozygous focal deletion of the PTEN tumor suppressor gene. These findings suggest that these tumors have a distinct oncogenesis from either common bile duct cancer or pancreatic cancer. Furthermore, this combination of genomic aberrations suggests a therapeutic context for dual mTOR/PI3K inhibition.
Whole genome sequencing can elucidate an oncogenic context and expose potential therapeutic vulnerabilities in rare cancers.
The incidence of melanoma is increasing more than any other cancer, and knowledge of its genetic alterations is limited. To systematically analyze such alterations, we performed whole-exome sequencing of 14 matched normal and metastatic tumor DNAs. Using stringent criteria, we identified 68 genes that appeared to be somatically mutated at elevated frequency, many of which are not known to be genetically altered in tumors. Most importantly, we discovered that TRRAP harbored a recurrent mutation that clustered in one position (p. Ser722Phe) in 6 out of 67 affected individuals (~4%), as well as a previously unidentified gene, GRIN2A, which was mutated in 33% of melanoma samples. The nature, pattern and functional evaluation of the TRRAP recurrent mutation suggest that TRRAP functions as an oncogene. Our study provides, to our knowledge, the most comprehensive map of genetic alterations in melanoma to date and suggests that the glutamate signaling pathway is involved in this disease.
Lung cancer is a leading cause of cancer related morbidity and mortality globally, and carries a dismal prognosis. Improved understanding of the biology of cancer is required to improve patient outcomes. Next-generation sequencing (NGS) is a powerful tool for whole genome characterisation, enabling comprehensive examination of somatic mutations that drive oncogenesis. Most NGS methods are based on polymerase chain reaction (PCR) amplification of platform-specific DNA fragment libraries, which are then sequenced. These techniques are well suited to high-throughput sequencing and are able to detect the full spectrum of genomic changes present in cancer. However, they require considerable investments in time, laboratory infrastructure, computational analysis and bioinformatic support. Next-generation sequencing has been applied to studies of the whole genome, exome, transcriptome and epigenome, and is changing the paradigm of lung cancer research and patient care. The results of this new technology will transform current knowledge of oncogenic pathways and provide molecular targets of use in the diagnosis and treatment of cancer. Somatic mutations in lung cancer have already been identified by NGS, and large scale genomic studies are underway. Personalised treatment strategies will improve care for those likely to benefit from available therapies, while sparing others the expense and morbidity of futile intervention. Organisational, computational and bioinformatic challenges of NGS are driving technological advances as well as raising ethical issues relating to informed consent and data release. Differentiation between driver and passenger mutations requires careful interpretation of sequencing data. Challenges in the interpretation of results arise from the types of specimens used for DNA extraction, sample processing techniques and tumour content. Tumour heterogeneity can reduce power to detect mutations implicated in oncogenesis. Next-generation sequencing will facilitate investigation of the biological and clinical implications of such variation. These techniques can now be applied to single cells and free circulating DNA, and possibly in the future to DNA obtained from body fluids and from subpopulations of tumour. As costs reduce, and speed and processing accuracy increase, NGS technology will become increasingly accessible to researchers and clinicians, with the ultimate goal of improving the care of patients with lung cancer.
High-throughput nucleotide sequencing; DNA sequence analysis; lung neoplasms; non-small cell lung carcinoma; small cell lung carcinoma
Gene fusions arising from chromosomal translocations have been implicated in cancer. However, the role of gene fusions in BRCA1-related breast cancers is not well understood. Mutations in BRCA1 are associated with an increased risk for breast cancer (up to 80% lifetime risk) and ovarian cancer (up to 50%). We sought to identify putative gene fusions in the transcriptomes of these cancers using high-throughput RNA sequencing (RNA-Seq).
We used Illumina sequencing technology to sequence the transcriptomes of five BRCA1-mutated breast cancer cell lines, three BRCA1-mutated primary tumors, two secretory breast cancer primary tumors and one non-tumorigenic breast epithelial cell line. Using a bioinformatics approach, our initial attempt at discovering putative gene fusions relied on analyzing single-end reads and identifying reads that aligned across exons of two different genes. Subsequently, latter samples were sequenced with paired-end reads and at longer cycles (producing longer reads). We then refined our approach by identifying misaligned paired reads, which may flank a putative gene fusion junction.
As a proof of concept, we were able to identify two previously characterized gene fusions in our samples using both single-end and paired-end approaches. In addition, we identified three novel in-frame fusions, but none were recurrent. Two of the candidates, WWC1-ADRBK2 in HCC3153 cell line and ADNP-C20orf132 in a primary tumor, were confirmed by Sanger sequencing and RT-PCR. RNA-Seq expression profiling of these two fusions showed a distinct overexpression of the 3' partner genes, suggesting that its expression may be under the control of the 5' partner gene's regulatory elements.
In this study, we used both single-end and paired-end sequencing strategies to discover gene fusions in breast cancer transcriptomes with BRCA1 mutations. We found that the use of paired-end reads is an effective tool for transcriptome profiling of gene fusions. Our findings suggest that while gene fusions are present in some BRCA1-mutated breast cancers, they are infrequent and not recurrent. However, private fusions may still be valuable as potential patient-specific biomarkers for diagnosis and treatment.
Glioblastoma multiforme, the most common type of primary brain tumor in adults, is driven by cells with neural stem (NS) cell characteristics. Using derivation methods developed for NS cells, it is possible to expand tumorigenic stem cells continuously in vitro. Although these glioblastoma-derived neural stem (GNS) cells are highly similar to normal NS cells, they harbor mutations typical of gliomas and initiate authentic tumors following orthotopic xenotransplantation. Here, we analyzed GNS and NS cell transcriptomes to identify gene expression alterations underlying the disease phenotype.
Sensitive measurements of gene expression were obtained by high-throughput sequencing of transcript tags (Tag-seq) on adherent GNS cell lines from three glioblastoma cases and two normal NS cell lines. Validation by quantitative real-time PCR was performed on 82 differentially expressed genes across a panel of 16 GNS and 6 NS cell lines. The molecular basis and prognostic relevance of expression differences were investigated by genetic characterization of GNS cells and comparison with public data for 867 glioma biopsies.
Transcriptome analysis revealed major differences correlated with glioma histological grade, and identified misregulated genes of known significance in glioblastoma as well as novel candidates, including genes associated with other malignancies or glioma-related pathways. This analysis further detected several long non-coding RNAs with expression profiles similar to neighboring genes implicated in cancer. Quantitative PCR validation showed excellent agreement with Tag-seq data (median Pearson r = 0.91) and discerned a gene set robustly distinguishing GNS from NS cells across the 22 lines. These expression alterations include oncogene and tumor suppressor changes not detected by microarray profiling of tumor tissue samples, and facilitated the identification of a GNS expression signature strongly associated with patient survival (P = 1e-6, Cox model).
These results support the utility of GNS cell cultures as a model system for studying the molecular processes driving glioblastoma and the use of NS cells as reference controls. The association between a GNS expression signature and survival is consistent with the hypothesis that a cancer stem cell component drives tumor growth. We anticipate that analysis of normal and malignant stem cells will be an important complement to large-scale profiling of primary tumors.
Genomic aberrations can be used to determine cancer diagnosis and prognosis. Clinically relevant novel aberrations can be discovered using high-throughput assays such as Single Nucleotide Polymorphism (SNP) arrays and next-generation sequencing, which typically provide aggregate signals of many cells at once. However, heterogeneity of tumor subclones dramatically complicates the task of detecting aberrations.
The aggregate signal of a population of subclones can be described as a linear system of equations. We employed a measure of allelic imbalance and total amount of DNA to characterize each locus by the copy number status (gain, loss or neither) of the strongest subclonal component. We designed simulated data to compare our measure to existing approaches and we analyzed SNP-arrays from 30 melanoma samples and transcriptome sequencing (RNA-Seq) from one melanoma sample.
We showed that any system describing aggregate subclonal signals is underdetermined, leading to non-unique solutions for the exact copy number profile of subclones. For this reason, our illustrative measure was more robust than existing Hidden Markov Model (HMM) based tools in inferring the aberration status, as indicated by tests on simulated data. This higher robustness contributed in identifying numerous aberrations in several loci of melanoma samples. We validated the heterogeneity and aberration status within single biopsies by fluorescent in situ hybridization of four affected and transcriptionally up-regulated genes E2F8, ETV4, EZH2 and FAM84B in 11 melanoma cell lines. Heterogeneity was further demonstrated in the analysis of allelic imbalance changes along single exons from melanoma RNA-Seq.
These studies demonstrate how subclonal heterogeneity, prevalent in tumor samples, is reflected in aggregate signals measured by high-throughput techniques. Our proposed approach yields high robustness in detecting copy number alterations using high-throughput technologies and has the potential to identify specific subclonal markers from next-generation sequencing data.
copy number; SNP arrays; Next generation sequencing; melanoma
Next generation sequencing (NGS) technologies have revolutionized cancer research allowing the comprehensive study of cancer using high throughput deep sequencing methodologies. These methods detect genomic alterations, nucleotide substitutions, insertions, deletions and copy number alterations. SOLiD (Sequencing by Oligonucleotide Ligation and Detection, Life Technologies) is a promising technology generating billions of 50 bp sequencing reads. This robust technique, successfully applied in gene identification, might be helpful in detecting novel genes associated with cancer initiation and progression using formalin fixed paraffin embedded (FFPE) tissue. This study’s aim was to compare the validity of whole exome sequencing of fresh-frozen vs. FFPE tumor tissue by normalization to normal prostatic FFPE tissue, obtained from the same patient. One primary fresh-frozen sample, corresponding FFPE prostate cancer sample and matched adjacent normal prostatic tissue was subjected to exome sequencing. The sequenced reads were mapped and compared. Our study was the first to show comparable exome sequencing results between FFPE and corresponding fresh-frozen cancer tissues using SOLiD sequencing. A prior study has been conducted comparing the validity of sequencing of FFPE vs. fresh frozen samples using other NGS platforms. Our validation further proves that FFPE material is a reliable source of material for whole exome sequencing.
exome sequencing; SOLiD4; prostate cancer; next-generation sequencing
The diagnosis and treatment of cancers, which rank among the leading causes of mortality in developed nations, presents substantial clinical challenges. The genetic and epigenetic heterogeneity of tumors can lead to differential response to therapy and gross disparities in patient outcomes, even for tumors originating from similar tissues. High-throughput DNA sequencing technologies hold promise to improve the diagnosis and treatment of cancers through efficient and economical profiling of complete tumor genomes, paving the way for approaches to personalized oncology that consider the unique genetic composition of the patient’s tumor. Here we present a novel method to leverage the information provided by cancer genome sequencing to match an individual tumor genome with commercial cell lines, which might be leveraged as clinical surrogates to inform prognosis or therapeutic strategy. We evaluate the method using a published lung cancer genome and genetic profiles of commercial cancer cell lines. The results support the general plausibility of this matching approach, thereby offering a first step in translational bioinformatics approaches to personalized oncology using established cancer cell lines.
Background. Next-generation sequencing of cancers has identified important therapeutic targets and biomarkers. The goal of this pilot study was to compare the genetic changes in a human papillomavirus- (HPV-)positive and an HPV-negative head and neck tumor.
Methods. DNA was extracted from the blood and primary tumor of a patient with an HPV-positive tonsillar cancer and those of a patient with an HPV-negative oral tongue tumor. Exome enrichment was performed using the Agilent SureSelect All Exon Kit, followed by sequencing on the ABI SOLiD platform.
Results. Exome sequencing revealed slightly more mutations in the HPV-negative tumor (73) in contrast to the HPV-positive tumor (58). Multiple mutations were noted in zinc finger genes (ZNF3, 10, 229, 470, 543, 616, 664, 638, 716, and 799) and mucin genes (MUC4, 6, 12, and 16). Mutations were noted in MUC12 in both tumors.
Conclusions. HPV-positive HNSCC is distinct from HPV-negative disease in terms of evidence of viral infection, p16 status, and frequency of mutations. Next-generation sequencing has the potential to identify novel therapeutic targets and biomarkers in HNSCC.
Gene fusions created by somatic genomic rearrangements are known to play an important role in the onset and development of some cancers, such as lymphomas and sarcomas. RNA-Seq (whole transcriptome shotgun sequencing) is proving to be a useful tool for the discovery of novel gene fusions in cancer transcriptomes. However, algorithmic methods for the discovery of gene fusions using RNA-Seq data remain underdeveloped. We have developed deFuse, a novel computational method for fusion discovery in tumor RNA-Seq data. Unlike existing methods that use only unique best-hit alignments and consider only fusion boundaries at the ends of known exons, deFuse considers all alignments and all possible locations for fusion boundaries. As a result, deFuse is able to identify fusion sequences with demonstrably better sensitivity than previous approaches. To increase the specificity of our approach, we curated a list of 60 true positive and 61 true negative fusion sequences (as confirmed by RT-PCR), and have trained an adaboost classifier on 11 novel features of the sequence data. The resulting classifier has an estimated value of 0.91 for the area under the ROC curve. We have used deFuse to discover gene fusions in 40 ovarian tumor samples, one ovarian cancer cell line, and three sarcoma samples. We report herein the first gene fusions discovered in ovarian cancer. We conclude that gene fusions are not infrequent events in ovarian cancer and that these events have the potential to substantially alter the expression patterns of the genes involved; gene fusions should therefore be considered in efforts to comprehensively characterize the mutational profiles of ovarian cancer transcriptomes.
Genome rearrangements and associated gene fusions are known to be important oncogenic events in some cancers. We have developed a novel computational method called deFuse for detecting gene fusions in RNA-Seq data and have applied it to the discovery of novel gene fusions in sarcoma and ovarian tumors. We assessed the accuracy of our method and found that deFuse produces substantially better sensitivity and specificity than two other published methods. We have also developed a set of 60 positive and 61 negative examples that will be useful for accurate identification of gene fusions in future RNA-Seq datasets. We have trained a classifier on 11 novel features of the 121 examples, and show that the classifier is able to accurately identify real gene fusions. The 45 gene fusions reported in this study represent the first ovarian cancer fusions reported, as well as novel sarcoma fusions. By examining the expression patterns of the affected genes, we find that many fusions are predicted to have functional consequences and thus merit experimental followup to determine their clinical relevance.
A subset of KIT/PDGFRA wild-type gastrointestinal stromal tumors (WT GIST) have been associated with alteration of the succinate dehydrogenase (SDH) complex II function. A recent report identified four non-syndromic, KIT/PDGFRA WT GIST harboring compound heterozygous or homozygous mutations in SDHA encoding the main subunit of the SDH complex II.
Next generation sequencing was applied on five pediatric and one young adult WT GIST, by whole exome capture and SOLiD 3-plus system sequencing. The putative mutations were first confirmed by Sanger sequencing and then screened on a larger panel of 11 pediatric and young adult WT GIST, including 5 in the context of Carney triad.
A germline p.Arg31X nonsense SDHA mutation was identified in one of the six cases tested by SOLiD platform. An additional p.D38V missense mutation in SDHA exon 2 was identified by Sanger sequencing in the extended KIT/PDGFRA WT GIST patients cohort. Western blotting showed loss of SDHA expression in the two cases harboring SDHA mutations, while expression being retained in the other WT GIST tumors. Results were further confirmed by immunohistochemistry for both SDHA and SDHB, which showed a concurrent loss of expression of both proteins in SDHA-mutant lesions, while the remaining WT tumors showed only loss of SDHB expression.
Germline and/or somatic aberrations of SDHA occur in a small subset of KIT/PDGFRA WT GISTs, outside the Carney’s triad and are associated with loss of both SDHA and SDHB protein expression. Mutations of the SDH complex II are more particularly associated with KIT/PDGFRA WT GIST occurring in young adults. Although pediatric GIST consistently display alterations of SDHB protein expression, further molecular studies are needed to identify the crucial genes involved in their tumorigenesis.
GIST; Pediatric; Wild-type; SDHA; SDHB; Succinate dehydrogenase complex II
Massively parallel sequencing enables the sequencing of whole genomes, exomes, and transcriptomes from many tumor samples. Thus, it now is possible to comprehensively identify somatic mutations, including single base changes, deletions, insertions, and genomic rearrangements. Early results for hematopoietic tumors show great promise but many questions remain to be answered.
To design a tool to assist clinician participation with cancer drug funding decisions. Public policy-makers and insurers are struggling with funding decisions regarding increasingly expensive new cancer drugs. Increasingly, oncologists are contributing to the process of review that leads to such decisions. We were asked to design a system for ranking new cancer drugs for priority-based funding decisions.
The “Accountability for Reasonableness” framework informed the design of a six-module multistakeholder decision process blending evidence-based traditional technology assessment methods with individual and cultural values elicitation. The tool was piloted in three settings: (1) videotaped simulated multistakeholder deliberation sessions; (2) clinical oncology leaders; and (3) a regional (Canadian provincial) pharmacy and therapeutics committee making formulary decisions. The modules involve: decision clarification, drug eligibility screening (filtering), clinical performance scoring index, cost modeling, data integration and values clarification, and process evaluation.
The tool was feasible to use, acceptable to participants, and able to rank candidate drugs. The pharmacy and therapeutics committee with whom it was tested used the tool as a part of their deliberations, and the tumor group leaders requested its incorporation into organization-based decision making.
The decision tool can facilitate priority-based cancer drug funding decisions that meet the conditions of fairness as perceived by participants, including oncologists.
Cancer results from somatic alterations in key genes, including point mutations, copy number alterations and structural rearrangements. A powerful way to discover cancer-causing genes is to identify genomic regions that show recurrent copy-number alterations (gains and losses) in tumor genomes. Recent advances in sequencing technologies suggest that massively parallel sequencing may provide a feasible alternative to DNA microarrays for detecting copy-number alterations. Here, we present: (i) a statistical analysis of the power to detect copy-number alterations of a given size; (ii) SegSeq, an algorithm to identify chromosomal breakpoints using massively parallel sequence data; and (iii) analysis of experimental data from three matched pairs of tumor and normal cell lines. We show that a collection of ∼14 million aligned sequence reads from human cell lines has comparable power to detect events as the current generation of DNA microarrays and has over two-fold better precision for localizing breakpoints (typically, to within ∼1 kb).
Formalin fixed paraffin embedded (FFPE) tissues are a vast resource of annotated clinical samples. As such, they represent highly desirable and informative materials for the application of high definition genomics for improved patient management and to advance the development of personalized therapeutics. However, a limitation of FFPE tissues is the variable quality of DNA extracted for analyses. Furthermore, admixtures of non-tumor and polyclonal neoplastic cell populations limit the number of biopsies that can be studied and make it difficult to define cancer genomes in patient samples. To exploit these valuable tissues we applied flow cytometry-based methods to isolate pure populations of tumor cell nuclei from FFPE tissues and developed a methodology compatible with oligonucleotide array CGH and whole exome sequencing analyses. These were used to profile a variety of tumors (breast, brain, bladder, ovarian and pancreas) including the genomes and exomes of matching fresh frozen and FFPE pancreatic adenocarcinoma samples.
It is generally accepted that cancers result from the aggregation of somatic mutations. The emergence of next-generation sequencing (NGS) technologies during the past half-decade has enabled studies of cancer genomes with high sensitivity and resolution through whole-genome and whole-exome sequencing approaches, among others. This saltatory advance introduces the possibility of assembling multiple cancer genomes for analysis in a cost-effective manner. Analytical approaches are now applied to the detection of a number of somatic genome alterations, including nucleotide substitutions, insertions/deletions, copy number variations, and chromosomal rearrangements. This review provides a thorough introduction to the cancer genomics pipeline as well as a case study of these methods put into practice.
cancer; genomics; sequencing; computation
Identification of somatic mutations in cancer is a major goal for understanding and monitoring the events related to cancer initiation and progression. High resolution melting (HRM) curve analysis represents a fast, post-PCR high-throughput method for scanning somatic sequence alterations in target genes. The aim of this study was to assess the sensitivity and specificity of HRM analysis for tumor mutation screening in a range of tumor samples, which included 216 frozen pediatric small rounded blue-cell tumors as well as 180 paraffin-embedded tumors from breast, endometrial and ovarian cancers (60 of each). HRM analysis was performed in exons of the following candidate genes known to harbor established commonly observed mutations: PIK3CA, ERBB2, KRAS, TP53, EGFR, BRAF, GATA3, and FGFR3. Bi-directional sequencing analysis was used to determine the accuracy of the HRM analysis. For the 39 mutations observed in frozen samples, the sensitivity and specificity of HRM analysis were 97% and 87%, respectively. There were 67 mutation/variants in the paraffin-embedded samples, and the sensitivity and specificity for the HRM analysis were 88% and 80%, respectively. Paraffin-embedded samples require higher quantity of purified DNA for high performance. In summary, HRM analysis is a promising moderate-throughput screening test for mutations among known candidate genomic regions. Although the overall accuracy appears to be better in frozen specimens, somatic alterations were detected in DNA extracted from paraffin-embedded samples.
It is well established that genomic alterations play an essential role in oncogenesis, disease progression, and response of tumors to therapeutic intervention. The advances of next-generation sequencing technologies (NGS) provide unprecedented capabilities to scan genomes for changes such as mutations, deletions, and alterations of chromosomal copy number. However, the cost of full-genome sequencing still prevents the routine application of NGS in many areas. Capturing and sequencing the coding exons of genes (the “exome”) can be a cost-effective approach for identifying changes that result in alteration of protein sequences. We applied an exome-sequencing technology (Roche Nimblegen capture paired with 454 sequencing) to identify sequence variation and mutations in eight commonly used cancer cell lines from a variety of tissue origins (A2780, A549, Colo205, GTL16, NCI-H661, MDA-MB468, PC3, and RD). We showed that this technology can accurately identify sequence variation, providing ∼95% concordance with Affymetrix SNP Array 6.0 performed on the same cell lines. Furthermore, we detected 19 of the 21 mutations reported in Sanger COSMIC database for these cell lines. We identified an average of 2,779 potential novel sequence variations/mutations per cell line, of which 1,904 were non-synonymous. Many non-synonymous changes were identified in kinases and known cancer-related genes. In addition we confirmed that the read-depth of exome sequence data can be used to estimate high-level gene amplifications and identify homologous deletions. In summary, we demonstrate that exome sequencing can be a reliable and cost-effective way for identifying alterations in cancer genomes, and we have generated a comprehensive catalogue of genomic alterations in coding regions of eight cancer cell lines. These findings could provide important insights into cancer pathways and mechanisms of resistance to anti-cancer therapies.
Recent whole-genome sequencing efforts led to the identification of IDH1R132 mutations in AML patients. We studied the prevalence and clinical implications of IDH1 genomic alterations in pediatric and adult AML. Diagnostic DNA from 531 AML patients treated on Children’s Oncology Group trial COG-AAML03P1 (N=257), and Southwest Oncology Group trials SWOG-9031, SWOG-9333, and SWOG-9500 (N=274), were tested for IDH1 mutations. Codon R132 mutations were absent in the pediatric cohort, but were found in 12/274 adult patients (4.4%, 95% CI 2.3-7.5%). IDH1R132 mutations occurred most commonly in patients with normal karyotype, and those with FLT3/ITD and NPMc mutations. Patients with IDH1R132 mutations trended towards higher median diagnostic WBC counts (59.2 × 109/L vs. 29.1 × 109/L, P=0.19) than those without mutations, but the two groups did not differ significantly in age, bone marrow blast percentage, overall survival, or relapse-free survival. Eleven patients (2.1%) harbored a novel V71I sequence alteration, which was found to be a germline polymorphism. IDH1 mutations were not detected in pediatric AML, and are uncommon in adult AML.
Acute Myeloid Leukemia; IDH1; isocitrate dehydrogenase; pediatric AML; NPM; FLT3
Cancer cells harbor a large number of molecular alterations such as mutations, amplifications and deletions on DNA sequences and epigenetic changes on DNA methylations. These aberrations may dysregulate gene expressions, which in turn drive the malignancy of tumors. Deciphering the causal and statistical relations of molecular aberrations and gene expressions is critical for understanding the molecular mechanisms of clinical phenotypes.
In this work, we proposed a computational method to reconstruct association modules containing driver aberrations, passenger mRNA or microRNA expressions, and putative regulators that mediate the effects from drivers to passengers. By applying the module-finding algorithm to the integrated datasets of NCI-60 cancer cell lines, we found that gene expressions were driven by diverse molecular aberrations including chromosomal segments' copy number variations, gene mutations and DNA methylations, microRNA expressions, and the expressions of transcription factors. In-silico validation indicated that passenger genes were enriched with the regulator binding motifs, functional categories or pathways where the drivers were involved, and co-citations with the driver/regulator genes. Moreover, 6 of 11 predicted MYB targets were down-regulated in an MYB-siRNA treated leukemia cell line. In addition, microRNA expressions were driven by distinct mechanisms from mRNA expressions.
The results provide rich mechanistic information regarding molecular aberrations and gene expressions in cancer genomes. This kind of integrative analysis will become an important tool for the diagnosis and treatment of cancer in the era of personalized medicine.
We explored diverse alterations contributing to liposarcomagenesis by sequencing the genome, exome, transcriptome, and cytosine methylome of a primary and recurrent dedifferentiated liposarcoma (DLPS) from distinct chemotherapy/radiotherapy-naïve patients. The liposarcoma genomes had complex structural rearrangements, but in different patterns, and with varied effects on the structure and expression of affected genes. While the point mutation rate was modest, integrative analyses and additional screening identified somatic mutations in HDAC1 in 8.3% of DLPS. Liposarcoma methylomes revealed alterations in differentiation pathway genes, including CEBPA methylation in 24% of DLPS. Treatment with demethylating agents, which restored CEBPA expression in DLPS cells, was anti-proliferative and pro-apoptotic in vitro and reduced tumor growth in vivo. Both genetic and epigenetic abnormalities established a role for small RNAs in liposarcomagenesis, typified by methylation-induced silencing of microRNA-193b in DLPS but not its well-differentiated counterpart. These findings reveal an unanticipated role for epigenetic abnormalities in DLPS tumors and suggest demethylating agents as potential therapeutics.
Dedifferentiated liposarcoma; DNA methylation; histone deacetylase; microRNA; adipocyte differentiation
Second Generation high-throughput sequencing technologies have revolutionized the genome sequencing applications and will ultimately have great impact on personalized medicine. The increase in capacity of both the AB/Life Technologies SOLiD 4.0 and Illumina HiSeq instrumentation and the ability of the platforms to multiplex samples has led to process innovations impacting many ongoing projects at the HGSC. Applications have ranged from regional and whole exome capture sequencing to the use of whole genome shotgun for deep coverage and determining structural rearrangements. Internal advancements have complemented the higher capacity instrumentation through the implementation of library automation, low DNA input samples, capture hybridization multiplexing and application of read mapping tools such as BFAST and BWA. Development of sample intake procedures, LIMS tracking and defined reporting metrics has enabled NexGen sequencing pipelines that can effectively deliver targeted and whole genome shotgun data for thousands of samples. These technical advancements to the pipeline have allowed us to achieve a rate of ∼1500 libraries/captures per month. To date the center has completed over 5000 exome and regional capture libraries for The Cancer Genome Atlas (TCGA), NIMH Autism, Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE-S) and 1000 Genomes Project. Development of these applications and methods will be discussed along with key data metrics, process management and pipeline organization.
Next-generation sequencing (also known as massively parallel sequencing) technologies are revolutionising our ability to characterise cancers at the genomic, transcriptomic and epigenetic levels. Cataloguing all mutations, copy number aberrations and somatic rearrangements in an entire cancer genome at base pair resolution can now be performed in a matter of weeks. Furthermore, massively parallel sequencing can be used as a means for unbiased transcriptomic analysis of mRNAs, small RNAs and noncoding RNAs, genome-wide methylation assays and high-throughput chromatin immunoprecipitation assays. Here, I discuss the potential impact of this technology on breast cancer research and the challenges that come with this technological breakthrough.