Chromothripsis in acute myeloid leukemia: Biological features and impact on survival

Chromothripsis is a one-step genome-shattering catastrophe resulting from disruption of one or few chromosomes in multiple fragments and consequent random rejoining and repair. This study defines incidence of chromothripsis in 395 newly diagnosed adult acute myeloid leukemia (AML) patients from three institutions, its impact on survival and its genomic background. SNP 6.0 or CytoscanHD Array (Affymetrix\uae) were performed on all samples. We detected chromothripsis with a custom algorithm in 26/395 patients. Patients harboring chromothripsis had higher age (p = 0.002), ELN high risk (HR) (p < 0.001), lower white blood cell (WBC) count (p = 0.040), TP53 loss, and/or mutations (p < 0.001) while FLT3 (p = 0.025), and NPM1 (p = 0.032) mutations were mutually exclusive with chromothripsis. Chromothripsis-positive patients showed a worse overall survival (OS) (p < 0.001) compared with HR patients (p = 0.011) and a poor prognosis in a COX-HR optimal regression model. Chromothripsis presented the hallmarks of chromosome instability [i.e., TP53 alteration, 5q deletion, higher mean of copy number alteration (CNA), complex karyotype, alterations in DNA repair, and cell cycle] and focal deletions on chromosomes 4, 7, 12, 16, and 17. CBA. FISH showed that chromothripsis is associated with marker, derivative, and ring chromosomes. In conclusion, chromothripsis frequently occurs in AML (6.6%) and influences patient prognosis and disease biology

Genomic landscape and chronological reconstruction of driver events in multiple myeloma

The multiple myeloma (MM) genome is heterogeneous and evolves through preclinical and post-diagnosis phases. Here we report a catalog and hierarchy of driver lesions using sequences from 67 MM genomes serially collected from 30 patients together with public exome datasets. Bayesian clustering defines at least 7 genomic subgroups with distinct sets of co-operating events. Focusing on whole genome sequencing data, complex structural events emerge as major drivers, including chromothripsis and a novel replication-based mechanism of templated insertions, which typically occur early. Hyperdiploidy also occurs early, with individual trisomies often acquired in different chronological windows during evolution, and with a preferred order of acquisition. Conversely, positively selected point mutations, whole genome duplication and chromoplexy events occur in later disease phases. Thus, initiating driver events, drawn from a limited repertoire of structural and numerical chromosomal changes, shape preferred trajectories of evolution that are biologically relevant but heterogeneous across patients

Complex Reorganization and Predominant Non-Homologous Repair Following Chromosomal Breakage in Karyotypically Balanced Germline Rearrangements and Transgenic Integration

We defined the genetic landscape of balanced chromosomal rearrangements at nucleotide resolution by sequencing 141 breakpoints from cytogenetically-interpreted translocations and inversions. We confirm that the recently described phenomenon of “chromothripsis” (massive chromosomal shattering and reorganization) is not unique to cancer cells but also occurs in the germline where it can resolve to a karyotypically balanced state with frequent inversions. We detected a high incidence of complex rearrangements (19.2%) and substantially less reliance on microhomology (31%) than previously observed in benign CNVs. We compared these results to experimentally-generated DNA breakage-repair by sequencing seven transgenic animals, and revealed extensive rearrangement of the transgene and host genome with similar complexity to human germline alterations. Inversion is the most common rearrangement, suggesting that a combined mechanism involving template switching and non-homologous repair mediates the formation of balanced complex rearrangements that are viable, stably replicated and transmitted unaltered to subsequent generations

Deep RNA sequencing analysis of readthrough gene fusions in human prostate adenocarcinoma and reference samples

<p>Abstract</p> <p>Background</p> <p>Readthrough fusions across adjacent genes in the genome, or transcription-induced chimeras (TICs), have been estimated using expressed sequence tag (EST) libraries to involve 4-6% of all genes. Deep transcriptional sequencing (RNA-Seq) now makes it possible to study the occurrence and expression levels of TICs in individual samples across the genome.</p> <p>Methods</p> <p>We performed single-end RNA-Seq on three human prostate adenocarcinoma samples and their corresponding normal tissues, as well as brain and universal reference samples. We developed two bioinformatics methods to specifically identify TIC events: a targeted alignment method using artificial exon-exon junctions within 200,000 bp from adjacent genes, and genomic alignment allowing splicing within individual reads. We performed further experimental verification and characterization of selected TIC and fusion events using quantitative RT-PCR and comparative genomic hybridization microarrays.</p> <p>Results</p> <p>Targeted alignment against artificial exon-exon junctions yielded 339 distinct TIC events, including 32 gene pairs with multiple isoforms. The false discovery rate was estimated to be 1.5%. Spliced alignment to the genome was less sensitive, finding only 18% of those found by targeted alignment in 33-nt reads and 59% of those in 50-nt reads. However, spliced alignment revealed 30 cases of TICs with intervening exons, in addition to distant inversions, scrambled genes, and translocations. Our findings increase the catalog of observed TIC gene pairs by 66%.</p> <p>We verified 6 of 6 predicted TICs in all prostate samples, and 2 of 5 predicted novel distant gene fusions, both private events among 54 prostate tumor samples tested. Expression of TICs correlates with that of the upstream gene, which can explain the prostate-specific pattern of some TIC events and the restriction of the <it>SLC45A3-ELK4 </it>e4-e2 TIC to <it>ERG</it>-negative prostate samples, as confirmed in 20 matched prostate tumor and normal samples and 9 lung cancer cell lines.</p> <p>Conclusions</p> <p>Deep transcriptional sequencing and analysis with targeted and spliced alignment methods can effectively identify TIC events across the genome in individual tissues. Prostate and reference samples exhibit a wide range of TIC events, involving more genes than estimated previously using ESTs. Tissue specificity of TIC events is correlated with expression patterns of the upstream gene. Some TIC events, such as <it>MSMB-NCOA4</it>, may play functional roles in cancer.</p

The apobec mutational activity in multiple myeloma: from diagnosis to cell lines

Next generation sequencing (NGS) studies have highlighted the role of aberrant activity of APOBEC DNA deaminases in generating the mu- tational repertoire of multiple myeloma (MM). However, the contribu- tion of this mutational process across the landscape of plasma cell dyscrasias, or its prognostic role, has never been investigated in detail. To answer these unexplored aspects of MM biology, we used published NGS data from our own work as well as others, including the large CoMMpass trial for a total of 1153 whole-exomes of MM. Furthermore, we investigated 5 MGUS, 6 primary plasma cell leukemias (pPCL) and 18 MM cell lines (MMCL). Overall, we identified signatures of two mu- tational processes, one related to spontaneous deamination of methy- lated cytosines (30% of variants, range 0-100%) and one attributed to aberrant APOBEC activity (70% of variants, range 0-100%). APOBEC contribution was extremely heterogeneous among MM patients, but was correlated with a higher mutational burden (r=0.71, p=<0.0001) and with MAF gene translocations t(14;16) and t(14;20). The activity of APOBEC increased from MGUS to MM to pPCL, both in terms of ab- solute number of mutations and as percentage contribution. In MMCL we instead observed a bi-modal distribution whereby 8 cell lines showed the highest numbers of mutations caused by APOBEC (5/8 car- ried MAF translocations), while 10 where virtually devoid of APOBEC mutations (0/10 carried MAF translocations). The contribution of APOBEC to the total mutational repertoire in MM had a clear prognos- tic impact. MM patients with APOBEC mutations in the lowest quartile had a survival advantage over patients with APOBEC mutations in the highest quartile both in terms of progression-free survival (3-y PFS 46% vs 67% months, p=<0.0001) and overall survival (3-y OS 52% vs 83%, p=0.0084). This association was retained in a multivariate model that included age, gender, cytogenetic class, ISS, and quartiles of mutational load both in PFS [p=0.02, HR 2.06 (95IC 1.11-3.81] and OS [p=0.02, HR 2.88 (95IC 1.17-7.09)]. Interestingly we found that APOBEC mutations in the 4th quartile retained its independent prognostic respect to high mutational load and presence of MAF translocations. Overall, our data suggest that APOBEC-mediated mutagenesis is strongly involved in MM pathogenesis and its activity persists during different phases of evolution, playing a critical role in MM genomic complexity, and im- pacting prognosis of the patients

Heterogeneity of genomic evolution and mutational profiles in multiple myeloma

Multiple myeloma is an incurable plasma cell malignancy with a complex and incompletely understood molecular pathogenesis. Here we use whole-exome sequencing, copy-number profiling and cytogenetics to analyse 84 myeloma samples. Most cases have a complex subclonal structure and show clusters of subclonal variants, including subclonal driver mutations. Serial sampling reveals diverse patterns of clonal evolution, including linear evolution, differential clonal response and branching evolution. Diverse processes contribute to the mutational repertoire, including kataegis and somatic hypermutation, and their relative contribution changes over time. We find heterogeneity of mutational spectrum across samples, with few recurrent genes. We identify new candidate genes, including truncations of SP140, LTB, ROBO1 and clustered missense mutations in EGR1. The myeloma genome is heterogeneous across the cohort, and exhibits diversity in clonal admixture and in dynamics of evolution, which may impact prognostic stratification, therapeutic approaches and assessment of disease response to treatment

Identification of cell-of-origin breast tumor subtypes in inflammatory breast cancer by gene expression profiling

Inflammatory breast cancer (IBC) is an aggressive form of locally advanced breast cancer with high metastatic potential. Most patients have lymph node involvement at the time of diagnosis and 1/3 of the patients have distant metastases. In a previous study, we demonstrated that IBC is a distinct form of breast cancer in comparison with non-IBC. The aim of this study was to investigate the presence of the different molecular subtypes in our data set of 16 IBC and 18 non-IBC specimen. Therefore, we selected an ‘intrinsic gene set’ of 144 genes, present on our cDNA chips and common to the ‘intrinsic gene set’ described by Sorlie et al. [PNAS, 2003]. This set of genes was tested for performance in the Norway/Stanford data set by unsupervised hierarchical clustering. Expression centroids were then calculated for the core members of each of the five subclasses in the Norway/Stanford data set and used to classify our own specimens by calculating Spearman correlations between each sample and each centroid. We identified the same cell-of-origin subtypes in IBC as those already described in non-IBC. The classification was in good agreement with immunohistochemical data for estrogen receptor protein expression and cytokeratin 5/6 protein expression. Confirmation was done by an alternative unsupervised hierarchical clustering method. The robustness of this classification was assessed by an unsupervised hierarchical clustering with an alternative gene set of 141 genes related to the cell-of-origin subtypes, selected using a discriminating score and iterative random permutation testing. The contribution of the different cell-of-origin subtypes to the IBC phenotype was investigated by principal component analysis. Generally, the combined ErbB2-overexpressing and basal-like cluster was more expressed in IBC compared to non-IBC, whereas the combined luminal A, luminal B and normal-like cluster was more pronounced in non-IBC compared to IBC. The presence of the same molecular cell-of-origin subtypes in IBC as in non-IBC does not exclude the specific molecular nature of IBC, since gene lists that characterize IBC and non-IBC are entirely different from gene lists that define the different cell-of-origin subtypes, as evidenced by principal component analysis.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/44236/1/10549_2005_Article_9015.pd

Breaking point: the genesis and impact of structural variation in tumours

Somatic structural variants undoubtedly play important roles in driving tumourigenesis. This is evident despite the substantial technical challenges that remain in accurately detecting structural variants and their breakpoints in tumours and in spite of our incomplete understanding of the impact of structural variants on cellular function. Developments in these areas of research contribute to the ongoing discovery of structural variation with a clear impact on the evolution of the tumour and on the clinical importance to the patient. Recent large whole genome sequencing studies have reinforced our impression of each tumour as a unique combination of mutations but paradoxically have also discovered similar genome-wide patterns of single-nucleotide and structural variation between tumours. Statistical methods have been developed to deconvolute mutation patterns, or signatures, that recur across samples, providing information about the mutagens and repair processes that may be active in a given tumour. These signatures can guide treatment by, for example, highlighting vulnerabilities in a particular tumour to a particular chemotherapy. Thus, although the complete reconstruction of the full evolutionary trajectory of a tumour genome remains currently out of reach, valuable data are already emerging to improve the treatment of cancer