Moritz Gerstung

Genomic Classification and Prognosis in Acute Myeloid Leukemia

BACKGROUND Recent studies have provided a detailed census of genes that are mutated in acute myeloid leukemia (AML). Our next challenge is to understand how this genetic diversity defines the pathophysiology of AML and informs clinical practice. METHODS We enrolled a total of 1540 patients in three prospective trials of intensive therapy. Combining driver mutations in 111 cancer genes with cytogenetic and clinical data, we defined AML genomic subgroups and their relevance to clinical outcomes. RESULTS We identified 5234 driver mutations across 76 genes or genomic regions, with 2 or more drivers identified in 86% of the patients. Patterns of co-mutation compartmentalized the cohort into 11 classes, each with distinct diagnostic features and clinical outcomes. In addition to currently defined AML subgroups, three heterogeneous genomic categories emerged: AML with mutations in genes encoding chromatin, RNA-splicing regulators, or both (in 18% of patients); AML with TP53 mutations, chromosomal aneuploidies, or both (in 13%); and, provisionally, AML with IDH2(R172) mutations (in 1%). Patients with chromatin-spliceosome and TP53-aneuploidy AML had poor outcomes, with the various class-defining mutations contributing independently and additively to the outcome. In addition to class-defining lesions, other co-occurring driver mutations also had a substantial effect on overall survival. The prognostic effects of individual mutations were often significantly altered by the presence or absence of other driver mutations. Such gene-gene interactions were especially pronounced for NPM1-mutated AML, in which patterns of co-mutation identified groups with a favorable or adverse prognosis. These predictions require validation in prospective clinical trials. CONCLUSIONS The driver landscape in AML reveals distinct molecular subgroups that reflect discrete paths in the evolution of AML, informing disease classification and prognostic stratification. (Funded by the Wellcome Trust and others; ClinicalTrials.gov number, NCT00146120.).

High burden and pervasive positive selection of somatic mutations in normal human skin

Normal skins curiously abnormal genome Within every tumor, a battle is being waged. As individual tumor cells acquire new mutations that promote their survival and growth, they clonally expand at the expense of tumor cells that are “less fit.” Martincorena et al. sequenced 234 biopsies of sun-exposed but physiologically normal skin from four individuals (see the Perspective by Brash). They found a surprisingly high burden of mutations, higher than that of many tumors. Many of the mutations known to drive the growth of cutaneous squamous cell carcinomas were already under strong positive selection. More than a quarter of normal skin cells carried a driver mutation, and every square centimeter of skin contained hundreds of competing mutant clones. Science, this issue p. 880; see also p. 867 Sun-exposed but physiologically normal human skin harbors an unexpectedly high number of cancer-causing mutations. [Also see Perspective by Brash] How somatic mutations accumulate in normal cells is central to understanding cancer development but is poorly understood. We performed ultradeep sequencing of 74 cancer genes in small (0.8 to 4.7 square millimeters) biopsies of normal skin. Across 234 biopsies of sun-exposed eyelid epidermis from four individuals, the burden of somatic mutations averaged two to six mutations per megabase per cell, similar to that seen in many cancers, and exhibited characteristic signatures of exposure to ultraviolet light. Remarkably, multiple cancer genes are under strong positive selection even in physiologically normal skin, including most of the key drivers of cutaneous squamous cell carcinomas. Positively selected mutations were found in 18 to 32% of normal skin cells at a density of ~140 driver mutations per square centimeter. We observed variability in the driver landscape among individuals and variability in the sizes of clonal expansions across genes. Thus, aged sun-exposed skin is a patchwork of thousands of evolving clones with over a quarter of cells carrying cancer-causing mutations while maintaining the physiological functions of epidermis.

Landscape of somatic mutations in 560 breast cancer whole-genome sequences

We analysed whole genome sequences of 560 breast cancers to advance understanding of the driver mutations conferring clonal advantage and the mutational processes generating somatic mutations. 93 protein-coding cancer genes carried likely driver mutations. Some non-coding regions exhibited high mutation frequencies but most have distinctive structural features probably causing elevated mutation rates and do not harbour driver mutations. Mutational signature analysis was extended to genome rearrangements and revealed 12 base substitution and six rearrangement signatures. Three rearrangement signatures, characterised by tandem duplications or deletions, appear associated with defective homologous recombination based DNA repair: one with deficient BRCA1 function; another with deficient BRCA1 or BRCA2 function; the cause of the third is unknown. This analysis of all classes of somatic mutation across exons, introns and intergenic regions highlights the repertoire of cancer genes and mutational processes operative, and progresses towards a comprehensive account of the somatic genetic basis of breast cancer.

Extensive transduction of nonrepetitive DNA mediated by L1 retrotransposition in cancer genomes

Introduction The human genome is peppered with mobile repetitive elements called long interspersed nuclear element–1 (L1) retrotransposons. Propagating through RNA and cDNA intermediates, these molecular parasites copy and insert themselves throughout the genome, with potentially disruptive effects on neighboring genes or regulatory sequences. In the germ line, unique sequence downstream of L1 elements can also be retrotransposed if transcription continues beyond the repeat, a process known as 3′ transduction. There has been growing interest in retrotransposition and 3′ transduction as a possible source of somatic mutations during tumorigenesis. The activity of individual L1 elements fluctuates during tumor evolution. In a lung tumor, hundreds of 3′ transductions arose from a small number of active L1 source elements (colored circles on outer rim of circle). As the tumor evolved from the preinvasive common ancestor to invasive cancer, individual elements exhibited variable activity over time. Rationale To explore whether 3′ transductions are frequent in cancer, we developed a bioinformatic algorithm for identifying somatically acquired retrotranspositions in cancer genomes. We applied our algorithm to 290 cancer samples from 244 patients across 12 tumor types. The unique downstream sequence mobilized with 3′ transductions effectively fingerprints the L1 source element, providing insights into the activity of individual L1 loci across the genome. Results Across the 290 samples, we identified 2756 somatic L1 retrotranspositions. Tumors from 53% of patients had at least one such event, with colorectal and lung cancers being most frequently affected (93% and 75% of patients, respectively). Somatic 3′ transductions comprised 24% of events, half of which represented mobilizations of unique sequence alone, without any accompanying L1 sequence. Overall, 95% of 3′ transductions identified derived from only 72 germline L1 source elements, with as few as four loci accounting for 50% of events. In a given sample, the same source element could generate 50 or more somatic transductions, scattered extensively across the genome. About 5% of somatic transductions arose from L1 source elements that were themselves somatic retrotranspositions. In three of the cases in which we sequenced more than one sample from a patient’s tumor, we were able to place 3′ transductions on the phylogenetic tree. We found that the activity of individual source elements fluctuated during tumor evolution, with different subclones exhibiting much variability in which elements were “on” and which were “off.” The ability to identify the individual L1 source elements active in a given tumor enabled us to study the promoter methylation of those elements specifically. We found that 3′ transduction activity in a patient’s tumor was always associated with hypomethylation of that element. Overall, 2.3% of transductions distributed exons or entire genes to other sites in the genome, and many more mobilized deoxyribonuclease I (DNAse-I) hypersensitive sites or transcription factor binding sites identified by the ENCODE project. Occasionally, somatic L1 insertions inserted near coding sequence and redistributed these exons elsewhere in the genome. However, we found no general effects of retrotranspositions on transcription levels of genes at the insertion points and no evidence for aberrant RNA species resulting from somatically acquired transposable elements. Indeed, as with germline retrotranspositions, somatic insertions exhibited a strong enrichment in heterochromatic, gene-poor regions of the genome. Conclusion Somatic 3′ transduction occurs frequently in human tumors, and in some cases transduction events can scatter exons, genes, and regulatory elements widely across the genome. Dissemination of these sequences appears to be due to a small number of highly active L1 elements, whose activity can wax and wane during tumor evolution. The majority of the retrotransposition events are likely to be harmless “passenger” mutations. Hitchhiking through the tumor genome Retrotransposons are DNA repeat sequences that are constantly on the move. By poaching certain cellular enzymes, they copy and insert themselves at new sites in the genome. Sometimes they carry along adjacent DNA sequences, a process called 3′ transduction. Tubio et al. found that 3′ transduction is a common event in human tumors. Because this process can scatter genes and regulatory sequences across the genome, it may represent yet another mechanism by which tumor cells acquire new mutations that help them survive and grow. Science, this issue p. 10.1126/science.1251343 Tumor genomes are peppered with mobile repeat sequences that carry along adjacent DNA when they insert into new genomic sites. Long interspersed nuclear element–1 (L1) retrotransposons are mobile repetitive elements that are abundant in the human genome. L1 elements propagate through RNA intermediates. In the germ line, neighboring, nonrepetitive sequences are occasionally mobilized by the L1 machinery, a process called 3′ transduction. Because 3′ transductions are potentially mutagenic, we explored the extent to which they occur somatically during tumorigenesis. Studying cancer genomes from 244 patients, we found that tumors from 53% of the patients had somatic retrotranspositions, of which 24% were 3′ transductions. Fingerprinting of donor L1s revealed that a handful of source L1 elements in a tumor can spawn from tens to hundreds of 3′ transductions, which can themselves seed further retrotranspositions. The activity of individual L1 elements fluctuated during tumor evolution and correlated with L1 promoter hypomethylation. The 3′ transductions disseminated genes, exons, and regulatory elements to new locations, most often to heterochromatic regions of the genome.

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. DOI: http://dx.doi.org/10.7554/eLife.02935.001

Combined hereditary and somatic mutations of replication error repair genes result in rapid onset of ultra-hypermutated cancers

DNA replication−associated mutations are repaired by two components: polymerase proofreading and mismatch repair. The mutation consequences of disruption to both repair components in humans are not well studied. We sequenced cancer genomes from children with inherited biallelic mismatch repair deficiency (bMMRD). High-grade bMMRD brain tumors exhibited massive numbers of substitution mutations (>250/Mb), which was greater than all childhood and most cancers (>7,000 analyzed). All ultra-hypermutated bMMRD cancers acquired early somatic driver mutations in DNA polymerase ɛ or δ. The ensuing mutation signatures and numbers are unique and diagnostic of childhood germ-line bMMRD (P < 10−13). Sequential tumor biopsy analysis revealed that bMMRD/polymerase-mutant cancers rapidly amass an excess of simultaneous mutations (∼600 mutations/cell division), reaching but not exceeding ∼20,000 exonic mutations in <6 months. This implies a threshold compatible with cancer-cell survival. We suggest a new mechanism of cancer progression in which mutations develop in a rapid burst after ablation of replication repair.

Polycomb preferentially targets stalled promoters of coding and noncoding transcripts.

The Polycomb group (PcG) and Trithorax group (TrxG) of proteins are required for stable and heritable maintenance of repressed and active gene expression states. Their antagonistic function on gene control, repression for PcG and activity for TrxG, is mediated by binding to chromatin and subsequent epigenetic modification of target loci. Despite our broad knowledge about composition and enzymatic activities of the protein complexes involved, our understanding still lacks important mechanistic detail and a comprehensive view on target genes. In this study we use an extensive data set of ChIP-seq, RNA-seq, and genome-wide detection of transcription start sites (TSSs) to identify and analyze thousands of binding sites for the PcG proteins and Trithorax from a Drosophila S2 cell line. In addition of finding a preference for stalled promoter regions of annotated genes, we uncover many intergenic PcG binding sites coinciding with nonannotated TSSs. Interestingly, this set includes previously unknown promoters for primary transcripts of microRNA genes, thereby expanding the scope of Polycomb control to noncoding RNAs essential for development, apoptosis, and growth.

Reliable detection of subclonal single-nucleotide variants in tumour cell populations

According to the clonal evolution model, tumour growth is driven by competing subclones in somatically evolving cancer cell populations, which gives rise to genetically heterogeneous tumours. Here we present a comparative targeted deep-sequencing approach combined with a customised statistical algorithm, called deepSNV, for detecting and quantifying subclonal single-nucleotide variants in mixed populations. We show in a rigorous experimental assessment that our approach is capable of detecting variants with frequencies as low as 1/10,000 alleles. In selected genomic loci of the TP53 and VHL genes isolated from matched tumour and normal samples of four renal cell carcinoma patients, we detect 24 variants at allele frequencies ranging from 0.0002 to 0.34. Moreover, we demonstrate how the allele frequencies of known single-nucleotide polymorphisms can be exploited to detect loss of heterozygosity. Our findings demonstrate that genomic diversity is common in renal cell carcinomas and provide quantitative evidence for the clonal evolution model.

Cancer Evolution: Mathematical Models and Computational Inference

Cancer is a somatic evolutionary process characterized by the accumulation of mutations, which contribute to tumor growth, clinical progression, immune escape, and drug resistance development. Evolutionary theory can be used to analyze the dynamics of tumor cell populations and to make inference about the evolutionary history of a tumor from molecular data. We review recent approaches to modeling the evolution of cancer, including population dynamics models of tumor initiation and progression, phylogenetic methods to model the evolutionary relationship between tumor subclones, and probabilistic graphical models to describe dependencies among mutations. Evolutionary modeling helps to understand how tumors arise and will also play an increasingly important prognostic role in predicting disease progression and the outcome of medical interventions, such as targeted therapy.

RUNX1 mutations in acute myeloid leukemia are associated with distinct clinico-pathologic and genetic features.

We evaluated the frequency, genetic architecture, clinico-pathologic features and prognostic impact of RUNX1 mutations in 2439 adult patients with newly-diagnosed acute myeloid leukemia (AML). RUNX1 mutations were found in 245 of 2439 (10%) patients; were almost mutually exclusive of AML with recurrent genetic abnormalities; and they co-occurred with a complex pattern of gene mutations, frequently involving mutations in epigenetic modifiers (ASXL1, IDH2, KMT2A, EZH2), components of the spliceosome complex (SRSF2, SF3B1) and STAG2, PHF6, BCOR. RUNX1 mutations were associated with older age (16–59 years: 8.5%; ⩾60 years: 15.1%), male gender, more immature morphology and secondary AML evolving from myelodysplastic syndrome. In univariable analyses, RUNX1 mutations were associated with inferior event-free (EFS, P<0.0001), relapse-free (RFS, P=0.0007) and overall survival (OS, P<0.0001) in all patients, remaining significant when age was considered. In multivariable analysis, RUNX1 mutations predicted for inferior EFS (P=0.01). The effect of co-mutation varied by partner gene, where patients with the secondary genotypes RUNX1mut/ASXL1mut (OS, P=0.004), RUNX1mut/SRSF2mut (OS, P=0.007) and RUNX1mut/PHF6mut (OS, P=0.03) did significantly worse, whereas patients with the genotype RUNX1mut/IDH2mut (OS, P=0.04) had a better outcome. In conclusion, RUNX1-mutated AML is associated with a complex mutation cluster and is correlated with distinct clinico-pathologic features and inferior prognosis.