RADIA: RNA and DNA Integrated Analysis for Somatic Mutation Detection

The detection of somatic single nucleotide variants is a crucial component to the characterization of the cancer genome. Mutation calling algorithms thus far have focused on comparing the normal and tumor genomes from the same individual. In recent years, it has become routine for projects like The Cancer Genome Atlas (TCGA) to also sequence the tumor RNA. Here we present RADIA (RNA and DNA Integrated Analysis), a novel computational method combining the patient-matched normal and tumor DNA with the tumor RNA to detect somatic mutations. The inclusion of the RNA increases the power to detect somatic mutations, especially at low DNA allelic frequencies. By integrating an individual’s DNA and RNA, we are able to detect mutations that would otherwise be missed by traditional algorithms that examine only the DNA. We demonstrate high sensitivity (84%) and very high precision (98% and 99%) for RADIA in patient data from endometrial carcinoma and lung adenocarcinoma from TCGA. Mutations with both high DNA and RNA read support have the highest validation rate of over 99%. We also introduce a simulation package that spikes in artificial mutations to patient data, rather than simulating sequencing data from a reference genome. We evaluate sensitivity on the simulation data and demonstrate our ability to rescue back mutations at low DNA allelic frequencies by including the RNA. Finally, we highlight mutations in important cancer genes that were rescued due to the incorporation of the RNA.     

Complex Patterns of Chromosome 11 Aberrations in Myeloid Malignancies Target CBL,MLL, DDB1 and LMO2

Exome sequencing of primary tumors identifies complex somatic mutation patterns. Assignment of relevance of individual somatic mutations is difficult and poses the next challenge for interpretation of next generation sequencing data. Here we present an approach how exome sequencing in combination with SNP microarray data may identify targets of chromosomal aberrations in myeloid malignancies. The rationale of this approach is that hotspots of chromosomal aberrations might also harbor point mutations in the target genes of deletions, gains or uniparental disomies (UPDs). Chromosome 11 is a frequent target of lesions in myeloid malignancies. Therefore, we studied chromosome 11 in a total of 813 samples from 773 individual patients with different myeloid malignancies by SNP microarrays and complemented the data with exome sequencing in selected cases exhibiting chromosome 11 defects. We found gains, losses and UPDs of chromosome 11 in 52 of the 813 samples (6.4%). Chromosome 11q UPDs frequently associated with mutations of CBL. In one patient the 11qUPD amplified somatic mutations in both CBL and the DNA repair gene DDB1. A duplication within MLL exon 3 was detected in another patient with 11qUPD. We identified several common deleted regions (CDR) on chromosome 11. One of the CDRs associated with de novo acute myeloid leukemia (P=0.013). One patient with a deletion at the LMO2 locus harbored an additional point mutation on the other allele indicating that LMO2 might be a tumor suppressor frequently targeted by 11p deletions. Our chromosome-centered analysis indicates that chromosome 11 contains a number of tumor suppressor genes and that the role of this chromosome in myeloid malignancies is more complex than previously recognized.     

Megalencephaly Syndromes: Exome Pipeline Strategies for Detecting Low-Level Mosaic Mutations

Two megalencephaly (MEG) syndromes, megalencephaly-capillary malformation (MCAP) and megalencephaly-polymicrogyriapolydactyly-hydrocephalus (MPPH), have recently been defined on the basis of physical and neuroimaging features. Subsequently, exome sequencing of ten MEG cases identified de-novo postzygotic mutations in PIK3CA which cause MCAP and de-novo mutations in AKT and PIK3R2 which cause MPPH. Here we present findings from exome sequencing three unrelated megalencephaly patients which identified a causal PIK3CA mutation in two cases and a causal PIK3R2 mutation in the third case. However, our patient with the PIK3R2 mutation which is considered to cause MPPH has a marked bifrontal band heterotopia which is a feature of MCAP. Furthermore, one of our patients with a PIK3CA mutation lacks syndactyly/polydactyly which is a characteristic of MCAP. These findings suggest that the overlap between MCAP and MPPH may be greater than the available studies suggest. In addition, the PIK3CA mutation in one of our patients could not be detected using standard exome analysis because the mutation was observed at a low frequency consistent with somatic mosaicism. We have therefore investigated several alternative methods of exome analysis and demonstrate that alteration of the initial allele frequency spectrum (AFS), used as a prior for variant calling in samtools, had the greatest power to detect variants with low mutant allele frequencies in our 3 MEG exomes and in simulated data. We therefore recommend non-default settings of the AFS in combination with stringent quality control when searching for causal mutation(s) that could have low levels of mutant reads due to post-zygotic mutation.     

Evaluating somatic tumor mutation detection without matched normal samples

Background

Observations of recurrent somatic mutations in tumors have led to identification and definition of signaling and other pathways that are important for cancer progression and therapeutic targeting. As tumor cells contain both an individual’s inherited genetic variants and somatic mutations, challenges arise in distinguishing these events in massively parallel sequencing datasets. Typically, both a tumor sample and a “normal” sample from the same individual are sequenced and compared; variants observed only in the tumor are considered to be somatic mutations. However, this approach requires two samples for each individual.

Results

We evaluate a method of detecting somatic mutations in tumor samples for which only a subset of normal samples are available. We describe tuning of the method for detection of mutations in tumors, filtering to remove inherited variants, and comparison of detected mutations to several matched tumor/normal analysis methods. Filtering steps include the use of population variation datasets to remove inherited variants as well a subset of normal samples to remove technical artifacts. We then directly compare mutation detection with tumor-only and tumor-normal approaches using the same sets of samples. Comparisons are performed using an internal targeted gene sequencing dataset (n = 3380) as well as whole exome sequencing data from The Cancer Genome Atlas project (n = 250). Tumor-only mutation detection shows similar recall (43–60%) but lesser precision (20–21%) to current matched tumor/normal approaches (recall 43–73%, precision 30–82%) when compared to a “gold-standard” tumor/normal approach. The inclusion of a small pool of normal samples improves precision, although many variants are still uniquely detected in the tumor-only analysis.

Conclusions

A detailed method for somatic mutation detection without matched normal samples enables study of larger numbers of tumor samples, as well as tumor samples for which a matched normal is not available. As sensitivity/recall is similar to tumor/normal mutation detection but precision is lower, tumor-only detection is more appropriate for classification of samples based on known mutations. Although matched tumor-normal analysis is preferred due to higher precision, we demonstrate that mutation detection without matched normal samples is possible for certain applications.

     

Absolute quantification of somatic DNA alterations in human cancer

We describe a computational method that infers tumor purity and malignant cell ploidy directly from analysis of somatic DNA alterations. The method, named ABSOLUTE, can detect subclonal heterogeneity and somatic homozygosity, and it can calculate statistical sensitivity for detection of specific aberrations. We used ABSOLUTE to analyze exome sequencing data from 214 ovarian carcinoma tumor-normal pairs. This analysis identified both pervasive subclonal somatic point-mutations and a small subset of predominantly clonal and homozygous mutations, which were overrepresented in the tumor suppressor genes TP53 and NF1 and in a candidate tumor suppressor gene CDK12. We also used ABSOLUTE to infer absolute allelic copy-number profiles from 3,155 diverse cancer specimens, revealing that genome-doubling events are common in human cancer, likely occur in cells that are already aneuploid, and influence pathways of tumor progression (for example, with recessive inactivation of NF1 being less common after genome doubling). ABSOLUTE will facilitate the design of clinical sequencing studies and studies of cancer genome evolution and intra-tumor heterogeneity.     

Real-Time Bidirectional Pyrophosphorolysis-Activated Polymerization for Quantitative Detection of Somatic Mutations

Detection of somatic mutations for targeted therapy is increasingly used in clinical settings. However, due to the difficulties of detecting rare mutations in excess of wild-type DNA, current methods often lack high sensitivity, require multiple procedural steps, or fail to be quantitative. We developed real-time bidirectional pyrophosphorolysis-activated polymerization (real-time Bi-PAP) that allows quantitative detection of somatic mutations. We applied the method to quantify seven mutations at codons 12 and 13 in KRAS, and 2 mutations (L858R, and T790M) in EGFR in clinical samples. The real-time Bi-PAP could detect 0.01% mutation in the presence of 100 ng template DNA. Of the 34 samples from the colon cancer patients, real-time Bi-PAP detected 14 KRAS mutant samples whereas the traditional real-time allele-specific PCR missed two samples with mutation abundance <1% and DNA sequencing missed nine samples with mutation abundance <10%. The detection results of the two EGFR mutations in 45 non-small cell lung cancer samples further supported the applicability of the real-time Bi-PAP. The real-time Bi-PAP also proved to be more efficient than the real-time allele-specific PCR in the detection of templates prepared from formalin-fixed paraffin-embedded samples. Thus, real-time Bi-PAP can be used for rapid and accurate quantification of somatic mutations. This flexible approach could be widely used for somatic mutation detection in clinical settings.     

体细胞变异对神经系统常见肿瘤和发育异常类疾病的致病性

在生物体发育过程中各种内源性及外源性因素均可造成DNA损伤,引起体细胞变异.研究表明体细胞变异对肿瘤具有致病性作用,而体细胞变异对神经系统发育异常类疾病的致病性鲜有报道.新一代测序技术的发展,尤其是全外显子测序,靶向深度测序的应用大大提高了低频体细胞变异检出的敏感性,使科研人员重新认识了体细胞变异在神经系统肿瘤和发育异常类疾病发生中的致病性.本文综述了体细胞变异在神经系统肿瘤和发育异常类疾病致病性方面的研究进展,旨在为今后研究该类疾病的遗传病因提供新的思路,同时也为新药开发提供理论依据.     

A Pathogenic Mosaic TP53 Mutation in Two Germ Layers Detected by Next Generation Sequencing

Background

Li-Fraumeni syndrome is caused by germline TP53 mutations and is clinically characterized by a predisposition to a range of cancers, most commonly sarcoma, brain tumours and leukemia. Pathogenic mosaic TP53 mutations have only rarely been described.

Methods and Findings

We describe a 2 years old child presenting with three separate cancers over a 6 month period; two soft tissue mesenchymal tumors and an aggressive metastatic neuroblastoma. As conventional testing of blood DNA by Sanger sequencing for mutations in TP53, ALK, and SDH was negative, whole exome sequencing of the blood DNA of the patient and both parents was performed to screen more widely for cancer predisposing mutations. In the patient''s but not the parents'' DNA we found a c.743 G>A, p.Arg248Gln (CCDS11118.1) TP53 mutation in 3–20% of sequencing reads, a level that would not generally be detectable by Sanger sequencing. Homozygosity for this mutation was detected in all tumor samples analyzed, and germline mosaicism was demonstrated by analysis of the child''s newborn blood spot DNA. The occurrence of separate tumors derived from different germ layers suggests that this de novo mutation occurred early in embryogenesis, prior to gastrulation.

Conclusion

The case demonstrates pathogenic mosaicim, detected by next generation deep sequencing, that arose in the early stages of embryogenesis.     

Quantitative and Sensitive Detection of GNAS Mutations Causing McCune-Albright Syndrome with Next Generation Sequencing

Somatic activating GNAS mutations cause McCune-Albright syndrome (MAS). Owing to low mutation abundance, mutant-specific enrichment procedures, such as the peptide nucleic acid (PNA) method, are required to detect mutations in peripheral blood. Next generation sequencing (NGS) can analyze millions of PCR amplicons independently, thus it is expected to detect low-abundance GNAS mutations quantitatively. In the present study, we aimed to develop an NGS-based method to detect low-abundance somatic GNAS mutations. PCR amplicons encompassing exons 8 and 9 of GNAS, in which most activating mutations occur, were sequenced on the MiSeq instrument. As expected, our NGS-based method could sequence the GNAS locus with very high read depth (approximately 100,000) and low error rate. A serial dilution study with use of cloned mutant and wildtype DNA samples showed a linear correlation between dilution and measured mutation abundance, indicating the reliability of quantification of the mutation. Using the serially diluted samples, the detection limits of three mutation detection methods (the PNA method, NGS, and combinatory use of PNA and NGS [PNA-NGS]) were determined. The lowest detectable mutation abundance was 1% for the PNA method, 0.03% for NGS and 0.01% for PNA-NGS. Finally, we analyzed 16 MAS patient-derived leukocytic DNA samples with the three methods, and compared the mutation detection rate of them. Mutation detection rate of the PNA method, NGS and PNA-NGS in 16 patient-derived peripheral blood samples were 56%, 63% and 75%, respectively. In conclusion, NGS can detect somatic activating GNAS mutations quantitatively and sensitively from peripheral blood samples. At present, the PNA-NGS method is likely the most sensitive method to detect low-abundance GNAS mutation.     

Mutational History of a Human Cell Lineage from Somatic to Induced Pluripotent Stem Cells

The accuracy of replicating the genetic code is fundamental. DNA repair mechanisms protect the fidelity of the genome ensuring a low error rate between generations. This sustains the similarity of individuals whilst providing a repertoire of variants for evolution. The mutation rate in the human genome has recently been measured to be 50–70 de novo single nucleotide variants (SNVs) between generations. During development mutations accumulate in somatic cells so that an organism is a mosaic. However, variation within a tissue and between tissues has not been analysed. By reprogramming somatic cells into induced pluripotent stem cells (iPSCs), their genomes and the associated mutational history are captured. By sequencing the genomes of polyclonal and monoclonal somatic cells and derived iPSCs we have determined the mutation rates and show how the patterns change from a somatic lineage in vivo through to iPSCs. Somatic cells have a mutation rate of 14 SNVs per cell per generation while iPSCs exhibited a ten-fold lower rate. Analyses of mutational signatures suggested that deamination of methylated cytosine may be the major mutagenic source in vivo, whilst oxidative DNA damage becomes dominant in vitro. Our results provide insights for better understanding of mutational processes and lineage relationships between human somatic cells. Furthermore it provides a foundation for interpretation of elevated mutation rates and patterns in cancer.     

A Massively Parallel Pipeline to Clone DNA Variants and Examine Molecular Phenotypes of Human Disease Mutations

Understanding the functional relevance of DNA variants is essential for all exome and genome sequencing projects. However, current mutagenesis cloning protocols require Sanger sequencing, and thus are prohibitively costly and labor-intensive. We describe a massively-parallel site-directed mutagenesis approach, “Clone-seq”, leveraging next-generation sequencing to rapidly and cost-effectively generate a large number of mutant alleles. Using Clone-seq, we further develop a comparative interactome-scanning pipeline integrating high-throughput GFP, yeast two-hybrid (Y2H), and mass spectrometry assays to systematically evaluate the functional impact of mutations on protein stability and interactions. We use this pipeline to show that disease mutations on protein-protein interaction interfaces are significantly more likely than those away from interfaces to disrupt corresponding interactions. We also find that mutation pairs with similar molecular phenotypes in terms of both protein stability and interactions are significantly more likely to cause the same disease than those with different molecular phenotypes, validating the in vivo biological relevance of our high-throughput GFP and Y2H assays, and indicating that both assays can be used to determine candidate disease mutations in the future. The general scheme of our experimental pipeline can be readily expanded to other types of interactome-mapping methods to comprehensively evaluate the functional relevance of all DNA variants, including those in non-coding regions.     

Exome Sequencing in Classic Hairy Cell Leukaemia Reveals Widespread Variation in Acquired Somatic Mutations between Individual Tumours Apart from the Signature BRAF V(600)E Lesion

In classic Hairy cell leukaemia (HCLc), a single case has thus far been interrogated by whole exome sequencing (WES) in a treatment naive patient, in which BRAF V(600)E was identified as an acquired somatic mutation and confirmed as occurring near-universally in this form of disease by conventional PCR-based cohort screens. It left open however the question whether other genome-wide mutations may also commonly occur at high frequency in presentation HCLc disease. To address this, we have carried out WES of 5 such typical HCLc cases, using highly purified splenic tumour cells paired with autologous T cells for germline. Apart from BRAF V(600)E, no other recurrent somatic mutation was identified in these HCLc exomes, thereby excluding additional acquired mutations as also prevalent at a near-universal frequency in this form of the disease. These data then place mutant BRAF at the centre of the neoplastic drive in HCLc. A comparison of our exome data with emerging genetic findings in HCL indicates that additional somatic mutations may however occur recurrently in smaller subsets of disease. As mutant BRAF alone is insufficient to drive malignant transformation in other histological cancers, it suggests that individual tumours utilise largely differing patterns of genetic somatic mutations to coalesce with BRAF V(600)E to drive pathogenesis of malignant HCLc disease.     

Combined Targeted DNA Sequencing in Non-Small Cell Lung Cancer (NSCLC) Using UNCseq and NGScopy,and RNA Sequencing Using UNCqeR for the Detection of Genetic Aberrations in NSCLC

The recent FDA approval of the MiSeqDx platform provides a unique opportunity to develop targeted next generation sequencing (NGS) panels for human disease, including cancer. We have developed a scalable, targeted panel-based assay termed UNCseq, which involves a NGS panel of over 200 cancer-associated genes and a standardized downstream bioinformatics pipeline for detection of single nucleotide variations (SNV) as well as small insertions and deletions (indel). In addition, we developed a novel algorithm, NGScopy, designed for samples with sparse sequencing coverage to detect large-scale copy number variations (CNV), similar to human SNP Array 6.0 as well as small-scale intragenic CNV. Overall, we applied this assay to 100 snap-frozen lung cancer specimens lacking same-patient germline DNA (07–0120 tissue cohort) and validated our results against Sanger sequencing, SNP Array, and our recently published integrated DNA-seq/RNA-seq assay, UNCqeR, where RNA-seq of same-patient tumor specimens confirmed SNV detected by DNA-seq, if RNA-seq coverage depth was adequate. In addition, we applied the UNCseq assay on an independent lung cancer tumor tissue collection with available same-patient germline DNA (11–1115 tissue cohort) and confirmed mutations using assays performed in a CLIA-certified laboratory. We conclude that UNCseq can identify SNV, indel, and CNV in tumor specimens lacking germline DNA in a cost-efficient fashion.     

Use of Targeted Exome Sequencing in Genetic Diagnosis of Chinese Familial Hypercholesterolemia

Familial hypercholesterolemia is an autosomal dominant inherited disease characterized by elevated plasma low-density lipoprotein cholesterol (LDL-C). It is mainly caused by mutations of the low-density lipoprotein receptor (LDLR) gene. Currently, the methods of whole genome sequencing or whole exome sequencing for screening mutations in familial hypercholesterolemia are not applicable in China due to high cost. We performed targeted exome sequencing of 167 genes implicated in the homozygous phenotype of a proband pedigree to identify candidate mutations, validated them in the family of the proband, studied the functions of the mutant protein, and followed up serum lipid levels after treatment. We discovered that exon 9 c.1268 T>C and exon 8 c.1129 T>G compound heterozygous mutations in the LDLR gene in the proband derived from the mother and father, respectively, in which the mutation of c.1129 T>G has not been reported previously. The mutant LDL-R protein had 57% and 52% binding and internalization functions, respectively, compared with that of the wild type. After 6 months of therapy, the LDL-C level of the proband decreased by more than 50% and the LDL-C of the other family members with heterozygous mutation also reduced to normal. Targeted exome sequencing is an effective method for screening mutation genes in familial hypercholesterolemia. The exon 8 and 9 mutations of the LDLR gene were pedigree mutations. The functions of the mutant LDL-R protein were decreased significantly compared with that of the wild type. Simvastatin plus ezetimibe was proven safe and effective in this preschool-age child.     

胞质异质性——人类肿瘤组织线粒体基因突变的普遍现象

为了探讨不同肿瘤组织中线粒体基因体细胞性突变的胞质异质性和同质性状态,利用32对重叠引物对149例肿瘤组织和匹配的正常组织的全线粒体基因进行PCR扩增,并同时进行时相温度梯度凝胶电泳扫描突变筛选,基因测序确定突变类型与异质状况。结果表明,不同肿瘤组织中线粒体基因体细胞性突变的异质率不同,口腔癌（65%）和食道癌（64%）具有较高的异质率,其次为乳腺癌（45.9%）。4种转换形式的发生频率Hm→Hm > Hm→Ht > Ht→Hm > Ht→Ht。碱基转换的主要转换形式为Hm→Hm,碱基颠换则以Hm→Ht。认为胞质异质性是人类肿瘤组织线粒体基因突变的普遍现象。Abstract: To explore the status of heteroplasmy and homoplasmy of Mitochondrial DNA somatic mutations in different tumors. DNA from 149 tumors and corresponding normal tissues were extracted and entire mitochondrial genome was amplified using 32 pairs of overlapping primers. The somatic mutations were screened by temporal temperature gradient gel electrophoresis and their heteroplasmic statute were identified by sequencing. The results showed that the incidence rate of heteroplasmy of mitochondrial DNA somatic mutations varies in different tumors. There is a high rate of heteroplasmic mutation in oral cancer (65%) and esophageal cancer (64%), followed by breast cancer (45%). The frequency of four transfer types is Hm (homoplasmy)→Hm (heteroplasmy) > Hm→Ht > Ht→Hm > Ht→Ht. The main transfer forms of transition and transversion mutations are Hm→Hm and Hm→Ht respectively. Heteroplasmy is a common phenomenon in mitochondrial DNA somatic mutations of human tumors.     

TP53 mutations,tetraploidy and homologous recombination repair defects in early stage high-grade serous ovarian cancer

To determine early somatic changes in high-grade serous ovarian cancer (HGSOC), we performed whole genome sequencing on a rare collection of 16 low stage HGSOCs. The majority showed extensive structural alterations (one had an ultramutated profile), exhibited high levels of p53 immunoreactivity, and harboured a TP53 mutation, deletion or inactivation. BRCA1 and BRCA2 mutations were observed in two tumors, with nine showing evidence of a homologous recombination (HR) defect. Combined Analysis with The Cancer Genome Atlas (TCGA) indicated that low and late stage HGSOCs have similar mutation and copy number profiles. We also found evidence that deleterious TP53 mutations are the earliest events, followed by deletions or loss of heterozygosity (LOH) of chromosomes carrying TP53, BRCA1 or BRCA2. Inactivation of HR appears to be an early event, as 62.5% of tumours showed a LOH pattern suggestive of HR defects. Three tumours with the highest ploidy had little genome-wide LOH, yet one of these had a homozygous somatic frame-shift BRCA2 mutation, suggesting that some carcinomas begin as tetraploid then descend into diploidy accompanied by genome-wide LOH. Lastly, we found evidence that structural variants (SV) cluster in HGSOC, but are absent in one ultramutated tumor, providing insights into the pathogenesis of low stage HGSOC.     

Confidence-based Somatic Mutation Evaluation and Prioritization

Next generation sequencing (NGS) has enabled high throughput discovery of somatic mutations. Detection depends on experimental design, lab platforms, parameters and analysis algorithms. However, NGS-based somatic mutation detection is prone to erroneous calls, with reported validation rates near 54% and congruence between algorithms less than 50%. Here, we developed an algorithm to assign a single statistic, a false discovery rate (FDR), to each somatic mutation identified by NGS. This FDR confidence value accurately discriminates true mutations from erroneous calls. Using sequencing data generated from triplicate exome profiling of C57BL/6 mice and B16-F10 melanoma cells, we used the existing algorithms GATK, SAMtools and SomaticSNiPer to identify somatic mutations. For each identified mutation, our algorithm assigned an FDR. We selected 139 mutations for validation, including 50 somatic mutations assigned a low FDR (high confidence) and 44 mutations assigned a high FDR (low confidence). All of the high confidence somatic mutations validated (50 of 50), none of the 44 low confidence somatic mutations validated, and 15 of 45 mutations with an intermediate FDR validated. Furthermore, the assignment of a single FDR to individual mutations enables statistical comparisons of lab and computation methodologies, including ROC curves and AUC metrics. Using the HiSeq 2000, single end 50 nt reads from replicates generate the highest confidence somatic mutation call set.