Somatic Mutations In Cancer Genomes Research Articles

Abstract SY25-01: Analysis of next-generation sequencing data for cancer genomes: challenges and pitfalls

Abstract The characterization of the landscape of genetic lesions that underlie cancer has been significantly advanced with the recent application of next-generation sequencing (NGS) technology. This methodology can be used to sequence selected subsets of genes, the whole exome, the whole genome, or the expressed transcriptome within a cancer cell. By comparing the acquired sequences from both a cancer and matched normal tissue sample from the same patients, one should be able to identify almost all somatic lesions within the cancer. As part of the St Jude Children's Research Hospital - Washington University Pediatric Cancer Genome Project (PCGP), we have undertaken the approach of performing whole genome sequencing (WGS) on 600 pediatric cancers and matched control tissue (1200 total genomes). Although the acquisition of the primary sequence is a formidable challenge, the analysis of these data is where the real work begins. Unfortunately, the majority of published NGS analysis methods were developed to identify germ line variation and therefore perform sub-optimally when applied to the task of identifying somatic mutations in cancer genomes. This is in part a result of the distinct difference in logic that must be used to accurately identify all somatic lesions within a cancer. A cancer genome typically exists within a heterogenous DNA sample that is composed of normal cells admixed with an oligoclonal tumor sample. Moreover, the range of somatic lesions seen in cancer is broader than what is seen as part of germ line genetic variation, with some cancers having exceedingly complex genomes containing focal insertions, deletions, inversions, intra-chromosomal and inter-chromosomal rearrangements and large copy number abnormalities. The accurate identification of these lesions requires not only the presence of the lesions within the cancer DNA, but also their absence from the matched germ line sample. To approach these problems, we, as well as others, have recently developed new analytical approaches to enhance our ability to identify the somatic mutations in cancer. The starting point for these analyses is ≥75 bp paired-end sequencing reads from patient matched tumor and normal DNA samples. Our goal is to identify all somatic single-nucleotide variation (SNV), small insertion/deletion (indel), copy number alteration (CNA) and structural variation (SV) that occur within the cancer DNA sample. Paired tumor-normal NGS data were analyzed together to ensure sensitivity for detecting DNA alterations in tumor and for confirming their absence in the matched normal sample. Somatic lesions initially identified by mapped NGS reads were further analyzed using more accurate algorithms to correct errors cause by suboptimal NGS mapping. The sensitivity of the methods we have developed depends on the read depth, but with WGS at 30X haploid coverage we are able to detect mono-allelic mutation present in as low as ∼25% of the analyzed cellular populations. This sensitivity can be significantly enhanced with greater read densities. Key among the methods we have developed are two new algorithms focused on identifying gross DNA alterations: CREST (Clipping REveals STructure) for SV analysis and CONSERTING (COpy Number SEgmentation by Regression Tree) for CNA analysis. CREST uses sequencing reads with partial alignments to the reference human genome (so-called soft-clipped reads) to directly map the breakpoints of somatic SVs. CONSERTING integrates read depth analysis with SV detection and adjust for sequencing artifacts, coverage bias and germ line CNVs. Together, these methods identify somatic lesions with a high validation rate (92-98% of SNV and Indels, 80% for SVs). In this talk, I will highlight the NGS analytic pipeline we have developed and the recent discoveries that have emerged through its application to pediatric cancer genomes. In addition, I will point out some of the significant challenges that remain to be tacked in order for us to identify the full landscape and functional consequences of the somatic mutations in cancer. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr SY25-01. doi:1538-7445.AM2012-SY25-01

The large-scale distribution of somatic mutations in cancer genomes

Recently, the genome sequences from several cancers have been published, along with the genome from a noncancer tissue from the same individual, allowing the identification of new somatic mutations in the cancer. We show that there is significant variation in the density of mutations at the 1-Mb scale within three cancer genomes and that the density of mutations is correlated between them. We also demonstrate that the density of mutations is correlated to that in the germline, as measured by the divergence between humans and chimpanzees and humans and macaques. We show that the density of mutations is correlated to the guanine and cytosine (GC) conent, replication time, distance to telomere and centromere, gene density, and nucleosome occupancy in the cancer genomes. However, overall, all factors explain less than 40% of the variance in mutation density and each factor explains very little of the variance. We find that genes associated with cancer occupy regions of the genome with significantly lower mutation rates than the average. Finally, we show that the density of mutations varies at a 10-Mb and a chromosomal scale, but that the variation at these scales is weak.

New therapeutic avenues in lung cancer based on disturbances in PI3K and RAS pathways

The vast majority of tumors, including lung cancer, have several alterations in the PI3K and RAS pathways, as well as in p53. The new challenge in providing better treatment for patients lies in understanding the relationship between different genetic abnormalities that can serve both as prognostic markers and as the basis for novel therapeutic intervention. For example, we have observed that AEG-1 and BRCA1 mRNA expression significantly influences progression-free survival (PFS), either in metastatic non-smallcell lung cancer (NSCLC) patients with or without EGFR mutations. BRCA1 mRNA expression was also an independent prognostic marker in erlotinibtreated EGFR mutated NSCLC patients, with significant differences in the length of PFS according to BRCA1 levels: median PFS was 27 months in patients with low BRCA1 mRNA expression [1]. Moreover, a significantly better PFS was obtained in those patients who had elevated expression of NFKBIA (a gatekeeper of the EGFR pathway and the inhibitor of NFuB activation) [2]. FBW7 is an important tumor suppressor gene that degrades mTORC1 and the loss of FBW7 could be a potential biomarker for treatment with mTOR pathway inhibitors [3]. FBW7 also inhibits the pro-survival protein MCL1 which is a crucial regulator of apoptosis triggered by antitubulin drugs. It has been suggested that profiling FBW7 and MCL1 status of tumors in terms of protein levels, messenger RNA levels and genetic status could be useful for predicting patient response to antitubulin drugs [4,5]. Intriguingly, in a systematic characterisation of somatic mutations in cancer genomes, a high frequency of FBW7 was observed in squamous cell lung cancer. However, in the report no details of the type of EGFR mutations are given [6]. Hotspot mutations have been described in FBW7 that occur in high frequencies, 30%, in cholangiocarcinomas and also in T-ALL [7]. Therefore, it is of great interest to examine FBW7 status, including mRNA expression. EZH2, an oncogene which activates NFuB and RAS, was closely correlated with BRCA1 expression in a series of 60 metastatic NSCLC patients, but with no correlation between levels of EZH2 and K-ras mutations. EZH2 mutations (Try641) have been reported in follicular and diffuse large B-cell lymphomas [8].

Finding co-mutated genes and candidate cancer genes in cancer genomes by stratified false discovery rate control

Finding candidate cancer genes playing causal roles in carcinogenesis is an important task in cancer research. The non-randomness of the co-mutation of genes in cancer samples can provide statistical evidence for these genes' involvement in carcinogenesis. It can also provide important information on the functional cooperation of gene mutations in cancer. However, due to the relatively small sample sizes used in current high-throughput somatic mutation screening studies and the extraordinary large-scale hypothesis tests, the statistical power of finding co-mutated gene pairs based on high-throughput somatic mutation data of cancer genomes is very low. Thus, we proposed a stratified FDR (False Discovery Rate) control approach, for identifying significantly co-mutated gene pairs according to the mutation frequency of genes. We then compared the identified co-mutated gene pairs separately by pre-selecting genes with higher mutation frequencies and by the stratified FDR control approach. Finally, we searched for pairs of pathways annotated with significantly more between-pathway co-mutated gene pairs to evaluate the functional roles of the identified co-mutated gene pairs. Based on two datasets of somatic mutations in cancer genomes, we demonstrated that, at a given FDR level, the power of finding co-mutated gene pairs could be increased by pre-selecting genes with higher mutation frequencies. However, many true co-mutation between genes with lower mutation rates will still be missed. By the stratified FDR control approach, many more co-mutated gene pairs could be found. Finally, the identified pathway pairs significantly overrepresented with between-pathway co-mutated gene pairs suggested that their co-dysregulations may play causal roles in carcinogenesis. The stratified FDR control strategy is efficient in identifying co-mutated gene pairs and the genes in the identified co-mutated gene pairs can be considered as candidate cancer genes because their non-random co-mutations in cancer genomes are highly unlikely to be attributable to chance.

Diverse somatic mutation patterns and pathway alterations in human cancers

The systematic characterization of somatic mutations in cancer genomes is essential for understanding the disease and for developing targeted therapeutics. Here we report the identification of 2,576 somatic mutations across approximately 1,800 megabases of DNA representing 1,507 coding genes from 441 tumours comprising breast, lung, ovarian and prostate cancer types and subtypes. We found that mutation rates and the sets of mutated genes varied substantially across tumour types and subtypes. Statistical analysis identified 77 significantly mutated genes including protein kinases, G-protein-coupled receptors such as GRM8, BAI3, AGTRL1 (also called APLNR) and LPHN3, and other druggable targets. Integrated analysis of somatic mutations and copy number alterations identified another 35 significantly altered genes including GNAS, indicating an expanded role for galpha subunits in multiple cancer types. Furthermore, our experimental analyses demonstrate the functional roles of mutant GNAO1 (a Galpha subunit) and mutant MAP2K4 (a member of the JNK signalling pathway) in oncogenesis. Our study provides an overview of the mutational spectra across major human cancers and identifies several potential therapeutic targets.

Abstract 1159: Single molecule sequencing to detect and characterize somatic mutations in cancer genomes

Abstract One result of large-scale cancer genome characterization is the identification of sets of genes that are commonly mutated in specific tumor types or subtypes and have clinical relevance, e.g. are prognostic or diagnostic. In this paradigm, we have investigated the use of a novel, single molecular real time (SMRT) sequencing technology from Pacific Biosciences that enables targeted regions to be sequenced in real time as single molecules in a mixed population. Several experiments were performed to evaluate the performance of this technology in the context of testing for cancer-specific mutations in previously characterized samples. In the first experiment we assessed whether SMRT sequencing could detect the known mutations of PCR products derived from genomic DNA of tumor cells compared to normal cells. In the second experiment we investigated the impact of different neoplastic cellularity percentages on the ability to detect known mutations. The final experiment involved producing deep read count SMRT sequencing data from PCR products containing known variants to ascertain their different levels of prevalence in a discrete tumor cell population, and then comparing these results to deep read counts for the same variants obtained with the Illumina instrument. Our results indicate that the Pacific Biosciences instrument offers exquisite sensitivity and speed in detecting somatic single base mutations in tumor-derived genomic DNAs. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 1159.

A comprehensive catalogue of somatic mutations in cancer genomes

This Hot Topics contribution considers two recently published papers that demonstrate the utility of advanced DNA sequencing technologies for identifying classes of mutations other than base substitutions. Data are presented from genome analyses of immortalized cell lines derived from a malignant melanoma and a small cell carcinoma of the lung. Among other observations the studies suggest the operation of novel DNA repair mechanisms or modes.