Hydroxymethylation profile of cell-free DNA is a biomarker for early colorectal cancer.

Early detection of cancer will improve survival rates. The blood biomarker 5-hydroxymethylcytosine has been shown to discriminate cancer. In a large covariate-controlled study of over two thousand individual blood samples, we created, tested and explored the properties of a 5-hydroxymethylcytosine-based classifier to detect colorectal cancer (CRC). In an independent validation sample set, the classifier discriminated CRC samples from controls with an area under the receiver operating characteristic curve (AUC) of 90% (95% CI [87, 93]). Sensitivity was 55% at 95% specificity. Performance was similar for early stage 1 (AUC 89%; 95% CI [83, 94]) and late stage 4 CRC (AUC 94%; 95% CI [89, 98]). The classifier could detect CRC even when the proportion of tumor DNA in blood was undetectable by other methods. Expanding the classifier to include information about cell-free DNA fragment size and abundance across the genome led to gains in sensitivity (63% at 95% specificity), with similar overall performance (AUC 91%; 95% CI [89, 94]). We confirm that 5-hydroxymethylcytosine can be used to detect CRC, even in early-stage disease. Therefore, the inclusion of 5-hydroxymethylcytosine in multianalyte testing could improve sensitivity for the detection of early-stage cancer.

Genome-wide analysis of copy number alterations led to the characterisation of PDCD10 as oncogene in ovarian cancer.

Copy Number Alterations (CNAs) represent the most common genetic alterations identified in ovarian cancer cells, being responsible for the extensive genomic instability observed in this cancer. Here we report the identification of CNAs in a cohort of Italian patients affected by ovarian cancer performed by SNP-based array. Our analysis allowed the identification of 201 significantly altered chromosomal bands (70 copy number gains; 131 copy number losses). The 3300 genes subjected to CNA identified here were compared to those present in the TCGA dataset. The analysis allowed the identification of 11 genes with increased CN and mRNA expression (PDCD10, EBAG9, NUDCD1, ENY2, CSNK2A1, TBC1D20, ZCCHC3, STARD3, C19orf12, POP4, UQCRFS1). PDCD10 was selected for further studies because of the highest frequency of CNA. PDCD10 was found, by immunostaining of three different Tissue Micro Arrays, to be over-expressed in the majority of ovarian primary cancer samples and in metastatic lesions. Moreover, significant correlations were found in specific subsets of patients, between increased PDCD10 expression and grade (p < 0.005), nodal involvement (p < 0.05) or advanced FIGO stage (p < 0.01). Finally, manipulation of PDCD10 expression by shRNA in ovarian cancer cells (OVCAR-5 and OVCA429) demonstrated a positive role for PDCD10 in the control of cell growth and motility in vitro and tumorigenicity in vivo. In conclusion, this study allowed the identification of novel genes subjected to copy number alterations in ovarian cancer. In particular, the results reported here point to a prominent role of PDCD10 as a bona fide oncogene.

A practical framework and online tool for mutational signature analyses show inter-tissue variation and driver dependencies.

Mutational signatures are patterns of mutations that arise during tumorigenesis. We present an enhanced, practical framework for mutational signature analyses. Applying these methods on 3,107 whole genome sequenced (WGS) primary cancers of 21 organs reveals known signatures and nine previously undescribed rearrangement signatures. We highlight inter-organ variability of signatures and present a way of visualizing that diversity, reinforcing our findings in an independent analysis of 3,096 WGS metastatic cancers. Signatures with a high level of genomic instability are dependent on TP53 dysregulation. We illustrate how uncertainty in mutational signature identification and assignment to samples affects tumor classification, reinforcing that using multiple orthogonal mutational signature data is not only beneficial, it is essential for accurate tumor stratification. Finally, we present a reference web-based tool for cancer and experimentally-generated mutational signatures, called Signal (https://signal.mutationalsignatures.com), that also supports performing mutational signature analyses.

The repertoire of mutational signatures in human cancer.

Somatic mutations in cancer genomes are caused by multiple mutational processes, each of which generates a characteristic mutational signature1. Here, as part of the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium2 of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA), we characterized mutational signatures using 84,729,690 somatic mutations from 4,645 whole-genome and 19,184 exome sequences that encompass most types of cancer. We identified 49 single-base-substitution, 11 doublet-base-substitution, 4 clustered-base-substitution and 17 small insertion-and-deletion signatures. The substantial size of our dataset, compared with previous analyses3-15, enabled the discovery of new signatures, the separation of overlapping signatures and the decomposition of signatures into components that may represent associated-but distinct-DNA damage, repair and/or replication mechanisms. By estimating the contribution of each signature to the mutational catalogues of individual cancer genomes, we revealed associations of signatures to exogenous or endogenous exposures, as well as to defective DNA-maintenance processes. However, many signatures are of unknown cause. This analysis provides a systematic perspective on the repertoire of mutational processes that contribute to the development of human cancer.

The Genomic and Immune Landscapes of Lethal Metastatic Breast Cancer.

The detailed molecular characterization of lethal cancers is a prerequisite to understanding resistance to therapy and escape from cancer immunoediting. We performed extensive multi-platform profiling of multi-regional metastases in autopsies from 10 patients with therapy-resistant breast cancer. The integrated genomic and immune landscapes show that metastases propagate and evolve as communities of clones, reveal their predicted neo-antigen landscapes, and show that they can accumulate HLA loss of heterozygosity (LOH). The data further identify variable tumor microenvironments and reveal, through analyses of T cell receptor repertoires, that adaptive immune responses appear to co-evolve with the metastatic genomes. These findings reveal in fine detail the landscapes of lethal metastatic breast cancer.

A Compendium of Mutational Signatures of Environmental Agents.

Whole-genome-sequencing (WGS) of human tumors has revealed distinct mutation patterns that hint at the causative origins of cancer. We examined mutational signatures in 324 WGS human-induced pluripotent stem cells exposed to 79 known or suspected environmental carcinogens. Forty-one yielded characteristic substitution mutational signatures. Some were similar to signatures found in human tumors. Additionally, six agents produced double-substitution signatures and eight produced indel signatures. Investigating mutation asymmetries across genome topography revealed fully functional mismatch and transcription-coupled repair pathways. DNA damage induced by environmental mutagens can be resolved by disparate repair and/or replicative pathways, resulting in an assortment of signature outcomes even for a single agent. This compendium of experimentally induced mutational signatures permits further exploration of roles of environmental agents in cancer etiology and underscores how human stem cell DNA is directly vulnerable to environmental agents. VIDEO ABSTRACT.

Partially methylated domains are hypervariable in breast cancer and fuel widespread CpG island hypermethylation.

Global loss of DNA methylation and CpG island (CGI) hypermethylation are key epigenomic aberrations in cancer. Global loss manifests itself in partially methylated domains (PMDs) which extend up to megabases. However, the distribution of PMDs within and between tumor types, and their effects on key functional genomic elements including CGIs are poorly defined. We comprehensively show that loss of methylation in PMDs occurs in a large fraction of the genome and represents the prime source of DNA methylation variation. PMDs are hypervariable in methylation level, size and distribution, and display elevated mutation rates. They impose intermediate DNA methylation levels incognizant of functional genomic elements including CGIs, underpinning a CGI methylator phenotype (CIMP). Repression effects on tumor suppressor genes are negligible as they are generally excluded from PMDs. The genomic distribution of PMDs reports tissue-of-origin and may represent tissue-specific silent regions which tolerate instability at the epigenetic, transcriptomic and genetic level.

GARFIELD classifies disease-relevant genomic features through integration of functional annotations with association signals.

Loci discovered by genome-wide association studies predominantly map outside protein-coding genes. The interpretation of the functional consequences of non-coding variants can be greatly enhanced by catalogs of regulatory genomic regions in cell lines and primary tissues. However, robust and readily applicable methods are still lacking by which to systematically evaluate the contribution of these regions to genetic variation implicated in diseases or quantitative traits. Here we propose a novel approach that leverages genome-wide association studies' findings with regulatory or functional annotations to classify features relevant to a phenotype of interest. Within our framework, we account for major sources of confounding not offered by current methods. We further assess enrichment of genome-wide association studies for 19 traits within Encyclopedia of DNA Elements- and Roadmap-derived regulatory regions. We characterize unique enrichment patterns for traits and annotations driving novel biological insights. The method is implemented in standalone software and an R package, to facilitate its application by the research community.

Author Correction: Landscape of somatic mutations in 560 breast cancer whole-genome sequences.

In the Methods section of this Article, 'greater than' should have been 'less than' in the sentence 'Putative regions of clustered rearrangements were identified as having an average inter-rearrangement distance that was at least 10 times greater than the whole-genome average for the individual sample. '. The Article has not been corrected.

Noncanonical secondary structures arising from non-B DNA motifs are determinants of mutagenesis.

Somatic mutations show variation in density across cancer genomes. Previous studies have shown that chromatin organization and replication time domains are correlated with, and thus predictive of, this variation. Here, we analyze 1809 whole-genome sequences from 10 cancer types to show that a subset of repetitive DNA sequences, called non-B motifs that predict noncanonical secondary structure formation can independently account for variation in mutation density. Combined with epigenetic factors and replication timing, the variance explained can be improved to 43%-76%. Approximately twofold mutation enrichment is observed directly within non-B motifs, is focused on exposed structural components, and is dependent on physical properties that are optimal for secondary structure formation. Therefore, there is mounting evidence that secondary structures arising from non-B motifs are not simply associated with increased mutation density-they are possibly causally implicated. Our results suggest that they are determinants of mutagenesis and increase the likelihood of recurrent mutations in the genome. This analysis calls for caution in the interpretation of recurrent mutations and highlights the importance of taking non-B motifs that can simply be inferred from the reference sequence into consideration in background models of mutability henceforth.

ChromoTrace: Computational reconstruction of 3D chromosome configurations for super-resolution microscopy.

The 3D structure of chromatin plays a key role in genome function, including gene expression, DNA replication, chromosome segregation, and DNA repair. Furthermore the location of genomic loci within the nucleus, especially relative to each other and nuclear structures such as the nuclear envelope and nuclear bodies strongly correlates with aspects of function such as gene expression. Therefore, determining the 3D position of the 6 billion DNA base pairs in each of the 23 chromosomes inside the nucleus of a human cell is a central challenge of biology. Recent advances of super-resolution microscopy in principle enable the mapping of specific molecular features with nanometer precision inside cells. Combined with highly specific, sensitive and multiplexed fluorescence labeling of DNA sequences this opens up the possibility of mapping the 3D path of the genome sequence in situ. Here we develop computational methodologies to reconstruct the sequence configuration of all human chromosomes in the nucleus from a super-resolution image of a set of fluorescent in situ probes hybridized to the genome in a cell. To test our approach, we develop a method for the simulation of DNA in an idealized human nucleus. Our reconstruction method, ChromoTrace, uses suffix trees to assign a known linear ordering of in situ probes on the genome to an unknown set of 3D in-situ probe positions in the nucleus from super-resolved images using the known genomic probe spacing as a set of physical distance constraints between probes. We find that ChromoTrace can assign the 3D positions of the majority of loci with high accuracy and reasonable sensitivity to specific genome sequences. By simulating appropriate spatial resolution, label multiplexing and noise scenarios we assess our algorithms performance. Our study shows that it is feasible to achieve genome-wide reconstruction of the 3D DNA path based on super-resolution microscopy images.

Corrigendum: A somatic-mutational process recurrently duplicates germline susceptibility loci and tissue-specific super-enhancers in breast cancers.

This corrects the article DOI: 10.1038/ng.3771.

Short inverted repeats contribute to localized mutability in human somatic cells.

Selected repetitive sequences termed short inverted repeats (SIRs) have the propensity to form secondary DNA structures called hairpins. SIRs comprise palindromic arm sequences separated by short spacer sequences that form the hairpin stem and loop respectively. Here, we show that SIRs confer an increase in localized mutability in breast cancer, which is domain-dependent with the greatest mutability observed within spacer sequences (∼1.35-fold above background). Mutability is influenced by factors that increase the likelihood of formation of hairpins such as loop lengths (of 4-5 bp) and stem lengths (of 7-15 bp). Increased mutability is an intrinsic property of SIRs as evidenced by how almost all mutational processes demonstrate a higher rate of mutagenesis of spacer sequences. We further identified 88 spacer sequences showing enrichment from 1.8- to 90-fold of local mutability distributed across 283 sites in the genome that intriguingly, can be used to inform the biological status of a tumor.

Whole-Genome Sequencing Reveals Breast Cancers with Mismatch Repair Deficiency.

Mismatch repair (MMR)-deficient cancers have been discovered to be highly responsive to immune therapies such as PD-1 checkpoint blockade, making their definition in patients, where they may be relatively rare, paramount for treatment decisions. In this study, we utilized patterns of mutagenesis known as mutational signatures, which are imprints of the mutagenic processes associated with MMR deficiency, to identify MMR-deficient breast tumors from a whole-genome sequencing dataset comprising a cohort of 640 patients. We identified 11 of 640 tumors as MMR deficient, but only 2 of 11 exhibited germline mutations in MMR genes or Lynch Syndrome. Two additional tumors had a substantially reduced proportion of mutations attributed to MMR deficiency, where the predominant mutational signatures were related to APOBEC enzymatic activity. Overall, 6 of 11 of the MMR-deficient cases in this cohort were confirmed genetically or epigenetically as having abrogation of MMR genes. However, IHC analysis of MMR-related proteins revealed all but one of 10 samples available for testing as MMR deficient. Thus, the mutational signatures more faithfully reported MMR deficiency than sequencing of MMR genes, because they represent a direct pathophysiologic readout of repair pathway abnormalities. As whole-genome sequencing continues to become more affordable, it could be used to expose individually abnormal tumors in tissue types where MMR deficiency has been rarely detected, but also rarely sought. Cancer Res; 77(18); 4755-62. ©2017 AACR.

Mutational Signatures in Breast Cancer: The Problem at the DNA Level.

A breast cancer genome is a record of the historic mutagenic activity that has occurred throughout the development of the tumor. Indeed, every mutation may be informative. Although driver mutations were the main focus of cancer research for a long time, passenger mutational signatures, the imprints of DNA damage and DNA repair processes that have been operative during tumorigenesis, are also biologically illuminating. This review is a chronicle of how the concept of mutational signatures arose and brings the reader up-to-date on this field, particularly in breast cancer. Mutational signatures have now been advanced to include mutational processes that involve rearrangements, and novel cancer biological insights have been gained through studying these in great detail. Furthermore, there are efforts to take this field into the clinical sphere. If validated, mutational signatures could thus form an additional weapon in the arsenal of cancer precision diagnostics and therapeutic stratification in the modern war against cancer. Clin Cancer Res; 23(11); 2617-29. ©2017 AACRSee all articles in this CCR Focus section, "Breast Cancer Research: From Base Pairs to Populations."

HRDetect is a predictor of BRCA1 and BRCA2 deficiency based on mutational signatures.

Approximately 1-5% of breast cancers are attributed to inherited mutations in BRCA1 or BRCA2 and are selectively sensitive to poly(ADP-ribose) polymerase (PARP) inhibitors. In other cancer types, germline and/or somatic mutations in BRCA1 and/or BRCA2 (BRCA1/BRCA2) also confer selective sensitivity to PARP inhibitors. Thus, assays to detect BRCA1/BRCA2-deficient tumors have been sought. Recently, somatic substitution, insertion/deletion and rearrangement patterns, or 'mutational signatures', were associated with BRCA1/BRCA2 dysfunction. Herein we used a lasso logistic regression model to identify six distinguishing mutational signatures predictive of BRCA1/BRCA2 deficiency. A weighted model called HRDetect was developed to accurately detect BRCA1/BRCA2-deficient samples. HRDetect identifies BRCA1/BRCA2-deficient tumors with 98.7% sensitivity (area under the curve (AUC) = 0.98). Application of this model in a cohort of 560 individuals with breast cancer, of whom 22 were known to carry a germline BRCA1 or BRCA2 mutation, allowed us to identify an additional 22 tumors with somatic loss of BRCA1 or BRCA2 and 47 tumors with functional BRCA1/BRCA2 deficiency where no mutation was detected. We validated HRDetect on independent cohorts of breast, ovarian and pancreatic cancers and demonstrated its efficacy in alternative sequencing strategies. Integrating all of the classes of mutational signatures thus reveals a larger proportion of individuals with breast cancer harboring BRCA1/BRCA2 deficiency (up to 22%) than hitherto appreciated (∼1-5%) who could have selective therapeutic sensitivity to PARP inhibition.

A somatic-mutational process recurrently duplicates germline susceptibility loci and tissue-specific super-enhancers in breast cancers.

Somatic rearrangements contribute to the mutagenized landscape of cancer genomes. Here, we systematically interrogated rearrangements in 560 breast cancers by using a piecewise constant fitting approach. We identified 33 hotspots of large (>100 kb) tandem duplications, a mutational signature associated with homologous-recombination-repair deficiency. Notably, these tandem-duplication hotspots were enriched in breast cancer germline susceptibility loci (odds ratio (OR) = 4.28) and breast-specific 'super-enhancer' regulatory elements (OR = 3.54). These hotspots may be sites of selective susceptibility to double-strand-break damage due to high transcriptional activity or, through incrementally increasing copy number, may be sites of secondary selective pressure. The transcriptomic consequences ranged from strong individual oncogene effects to weak but quantifiable multigene expression effects. We thus present a somatic-rearrangement mutational process affecting coding sequences and noncoding regulatory elements and contributing a continuum of driver consequences, from modest to strong effects, thereby supporting a polygenic model of cancer development.

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. We found that 93 protein-coding cancer genes carried probable driver mutations. Some non-coding regions exhibited high mutation frequencies, but most have distinctive structural features probably causing elevated mutation rates and do not contain driver mutations. Mutational signature analysis was extended to genome rearrangements and revealed twelve base substitution and six rearrangement signatures. Three rearrangement signatures, characterized 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 operating, and progresses towards a comprehensive account of the somatic genetic basis of breast cancer.