Mechanistic origins of diverse genome rearrangements in cancer.

Cancer genomes frequently harbor structural chromosomal rearrangements that disrupt the linear DNA sequence order and copy number. To date, diverse classes of structural variants have been identified across multiple cancer types. These aberrations span a wide spectrum of complexity, ranging from simple translocations to intricate patterns of rearrangements involving multiple chromosomes. Although most somatic rearrangements are acquired gradually throughout tumorigenesis, recent interrogation of cancer genomes have uncovered novel categories of complex rearrangements that arises rapidly through a one-off catastrophic event, including chromothripsis and chromoplexy. Here we review the cellular and molecular mechanisms contributing to the formation of diverse structural rearrangement classes during cancer development. Genotoxic stress from a myriad of extrinsic and intrinsic sources can trigger DNA double-strand breaks that are subjected to DNA repair with potentially mutagenic outcomes. We also highlight how aberrant nuclear structures generated through mitotic cell division errors, such as rupture-prone micronuclei and chromosome bridges, can instigate massive DNA damage and the formation of complex rearrangements in cancer genomes.

Landscape and Saturation Analysis of Mutations Associated With Race in Cancer Genomes by Clinical Sequencing.

Differences in cancer genomes between racial groups may impact tumor biology and health disparities. However, the discovery of race-associated mutations is constrained by the limited representation and sample size of different racial groups in prior genomic studies. We evaluated the influence of race on the frequency of gene mutations using the Genomics, Evidence, Neoplasia, Information, Exchange database, a large genomic dataset aggregated from clinical sequencing. Matched cohort analyses were used to identify histology-specific race-associated mutations including increased TERT promoter mutations in Black and Asian patients with gliomas and bladder cancers, and a decreased frequency of mutations in DNA repair pathway genes and subunits of the SWI/SNF chromatin complex in Asian and Black patients across multiple cancer types. The distribution of actionable mutations in oncogenes was also race-specific, demonstrating how targeted therapies may have a disparate impact on racial groups. Down-sampling analyses indicate that larger sample sizes are likely to discover more race-associated mutations. These results provide a resource to understand differences in cancer genomes between racial groups which may inform the design of clinical studies and patient recruitment strategies in biomarker trials.

When Genome Maintenance Goes Badly Awry.

Genetic abnormalities are present in all tumor types, although the frequency and type can vary. Chromosome abnormalities include highly aberrant structures, particularly chromothriptic chromosomes. The generation of massive sequencing data has illuminated the scope of the mutational burden in cancer genomes, identifying patterns of mutations (mutation signatures), which have the potential to shed light on the relatedness and etiologies of cancers and impact therapy response. Some mutation patterns are clearly attributable to disruptions in pathways that maintain genomic integrity. Here we review recent advances in our understanding of genetic changes occurring in cancers and the roles of genome maintenance pathways.

Integrated analysis of co-expression and ceRNA network identifies five lncRNAs as prognostic markers for breast cancer.

Long non-coding RNAs (lncRNAs), which competitively bind miRNAs to regulate target mRNA expression in the competing endogenous RNAs (ceRNAs) network, have attracted increasing attention in breast cancer research. We aim to find more effective therapeutic targets and prognostic markers for breast cancer. LncRNA, mRNA and miRNA expression profiles of breast cancer were downloaded from TCGA database. We screened the top 5000 lncRNAs, top 5000 mRNAs and all miRNAs to perform weighted gene co-expression network analysis. The correlation between modules and clinical information of breast cancer was identified by Pearson's correlation coefficient. Based on the most relevant modules, we constructed a ceRNA network of breast cancer. Additionally, the standard Kaplan-Meier univariate curve analysis was adopted to identify the prognosis of lncRNAs. Ultimately, a total of 23 and 5 modules were generated in the lncRNAs/mRNAs and miRNAs co-expression network, respectively. According to the Green module of lncRNAs/mRNAs and Blue module of miRNAs, our constructed ceRNA network consisted of 52 lncRNAs, 17miRNAs and 79 mRNAs. Through survival analysis, 5 lncRNAs (AL117190.1, COL4A2-AS1, LINC00184, MEG3 and MIR22HG) were identified as crucial prognostic factors for patients with breast cancer. Taken together, we have identified five novel lncRNAs related to prognosis of breast cancer. Our study has contributed to the deeper understanding of the molecular mechanism of breast cancer and provided novel insights into the use of breast cancer drugs and prognosis.

Tissue-specific impact of stem-loops and quadruplexes on cancer breakpoints formation.

BACKGROUND: Chromosomal rearrangements are the typical phenomena in cancer genomes causing gene disruptions and fusions, corruption of regulatory elements, damage to chromosome integrity. Among the factors contributing to genomic instability are non-B DNA structures with stem-loops and quadruplexes being the most prevalent. We aimed at investigating the impact of specifically these two classes of non-B DNA structures on cancer breakpoint hotspots using machine learning approach. METHODS: We developed procedure for machine learning model building and evaluation as the considered data are extremely imbalanced and it was required to get a reliable estimate of the prediction power. We built logistic regression models predicting cancer breakpoint hotspots based on the densities of stem-loops and quadruplexes, jointly and separately. We also tested Random Forest models varying different resampling schemes (leave-one-out cross validation, train-test split, 3-fold cross-validation) and class balancing techniques (oversampling, stratification, synthetic minority oversampling). RESULTS: We performed analysis of 487,425 breakpoints from 2234 samples covering 10 cancer types available from the International Cancer Genome Consortium. We showed that distribution of breakpoint hotspots in different types of cancer are not correlated, confirming the heterogeneous nature of cancer. It appeared that stem-loop-based model best explains the blood, brain, liver, and prostate cancer breakpoint hotspot profiles while quadruplex-based model has higher performance for the bone, breast, ovary, pancreatic, and skin cancer. For the overall cancer profile and uterus cancer the joint model shows the highest performance. For particular datasets the constructed models reach high predictive power using just one predictor, and in the majority of the cases, the model built on both predictors does not increase the model performance. CONCLUSION: Despite the heterogeneity in breakpoint hotspots' distribution across different cancer types, our results demonstrate an association between cancer breakpoint hotspots and stem-loops and quadruplexes. Approximately for half of the cancer types stem-loops are the most influential factors while for the others these are quadruplexes. This fact reflects the differences in regulatory potential of stem-loops and quadruplexes at the tissue-specific level, which yet to be discovered at the genome-wide scale. The performed analysis demonstrates that influence of stem-loops and quadruplexes on breakpoint hotspots formation is tissue-specific.

Dark kinase annotation, mining, and visualization using the Protein Kinase Ontology.

The Protein Kinase Ontology (ProKinO) is an integrated knowledge graph that conceptualizes the complex relationships among protein kinase sequence, structure, function, and disease in a human and machine-readable format. In this study, we have significantly expanded ProKinO by incorporating additional data on expression patterns and drug interactions. Furthermore, we have developed a completely new browser from the ground up to render the knowledge graph visible and interactive on the web. We have enriched ProKinO with new classes and relationships that capture information on kinase ligand binding sites, expression patterns, and functional features. These additions extend ProKinO's capabilities as a discovery tool, enabling it to uncover novel insights about understudied members of the protein kinase family. We next demonstrate the application of ProKinO. Specifically, through graph mining and aggregate SPARQL queries, we identify the p21-activated protein kinase 5 (PAK5) as one of the most frequently mutated dark kinases in human cancers with abnormal expression in multiple cancers, including a previously unappreciated role in acute myeloid leukemia. We have identified recurrent oncogenic mutations in the PAK5 activation loop predicted to alter substrate binding and phosphorylation. Additionally, we have identified common ligand/drug binding residues in PAK family kinases, underscoring ProKinO's potential application in drug discovery. The updated ontology browser and the addition of a web component, ProtVista, which enables interactive mining of kinase sequence annotations in 3D structures and Alphafold models, provide a valuable resource for the signaling community. The updated ProKinO database is accessible at https://prokino.uga.edu.

Mitochondrial genome sequencing of marine leukaemias reveals cancer contagion between clam species in the Seas of Southern Europe.

Clonally transmissible cancers are tumour lineages that are transmitted between individuals via the transfer of living cancer cells. In marine bivalves, leukaemia-like transmissible cancers, called hemic neoplasia (HN), have demonstrated the ability to infect individuals from different species. We performed whole-genome sequencing in eight warty venus clams that were diagnosed with HN, from two sampling points located more than 1000 nautical miles away in the Atlantic Ocean and the Mediterranean Sea Coasts of Spain. Mitochondrial genome sequencing analysis from neoplastic animals revealed the coexistence of haplotypes from two different clam species. Phylogenies estimated from mitochondrial and nuclear markers confirmed this leukaemia originated in striped venus clams and later transmitted to clams of the species warty venus, in which it survives as a contagious cancer. The analysis of mitochondrial and nuclear gene sequences supports all studied tumours belong to a single neoplastic lineage that spreads in the Seas of Southern Europe.

In humans and other animals, cancer cells divide excessively, forming tumours or flooding the blood, but they rarely spread to other individuals. However, some animals, including dogs, Tasmanian devils and bivalve molluscs like clams, cockles and mussels, can develop cancers that are transmitted from one individual to another. Despite these cancers being contagious, each one originates in a single animal, meaning that even when the cancer has spread to many individuals, its origins can be traced through its DNA. Cancer contagion is rare, but transmissible cancers seem to be particularly common in the oceans. In fact, 7 types of contagious cancer have been described in bivalve species so far. These cancers are known as 'hemic neoplasias', and are characterized by the uncontrolled division of blood-like cells, which can be released by the host they developed in, and survive in ocean water. When these cells encounter individuals from the same species, they can infect them, causing them to develop hemic neoplasia too There are still many unanswered questions about contagious cancers in bivalves. For example, how many species do the cancers affect, and which species do the cancers originate in? To address these questions, Garcia-Souto, Bruzos, Díaz et al. gathered over 400 specimens of a species of clam called the warty venus clam from the coastlines of Europe and examined them for signs of cancer. Clams collected in two regions of Spain showed signs of hemic neoplasia: one of the populations was from the Balearic Islands in the Mediterranean Sea, while the other came from the Atlantic coast of northwestern Spain. Analyzing the genomes of the tumours from each population showed that the cancer cells from both regions had likely originated in the same animal, indicating that the cancer is contagious and had spread through different populations. The analysis also revealed that the cancer did not originally develop in warty venus clams: the cancer cells contained DNA from both warty venus clams and another species called striped venus clams. These two species live close together in the Mediterranean Sea, suggesting that the cancer started in a striped venus clam and then spread to a warty venus clam. To determine whether the cancer still affected both species, Garcia-Souto, Bruzos, Díaz et al. screened 200 striped venus clams from the same areas, but no signs of cancer were found in these clams. This suggests that currently the cancer only affects the warty venus clam. These findings confirm that contagious cancers can jump between clam species, which could be threat to the marine environment. The fact that the cancer was so similar in clams from the Atlantic coast and from the Mediterranean Sea, however, suggests that it may have emerged very recently, or that human activity helped it to spread from one place to another. If the latter is the case, it may be possible to prevent further spread of these sea-borne cancers through human intervention.

New York's Polyethnic-1000: a regional initiative to understand how diverse ancestries influence the risk, progression, and treatment of cancers.

Research consortia can help to repair deficiencies in knowledge about the influence of inherited genetic diversity on disease. The New York Genome Center (NYGC) recently established Polyethnic-1000 (P-1000), a multi-institutional collaboration to study hereditary factors affecting several types of cancer. Here, we describe its rationale, organization, development, current activities, and prospects.

CTCF: A misguided jack-of-all-trades in cancer cells.

The emergence and progression of cancers is accompanied by a dysregulation of transcriptional programs. The three-dimensional (3D) organization of the human genome has emerged as an important multi-level mediator of gene transcription and regulation. In cancer cells, this organization can be restructured, providing a framework for the deregulation of gene activity. The CTCF protein, initially identified as the product from a tumor suppressor gene, is a jack-of-all-trades for the formation of 3D genome organization in normal cells. Here, we summarize how CTCF is involved in the multi-level organization of the human genome and we discuss emerging insights into how perturbed CTCF function and DNA binding causes the activation of oncogenes in cancer cells, mostly through a process of enhancer hijacking. Moreover, we highlight non-canonical functions of CTCF that can be relevant for the emergence of cancers as well. Finally, we provide guidelines for the computational identification of perturbed CTCF binding and reorganized 3D genome structure in cancer cells.

From structure to function, how bioinformatics help to reveal functions of our genomes.

A report on the 13th International Bioinformatics Workshop held in Harbin, China, 5-6 August 2017.

Network-Based Coverage of Mutational Profiles Reveals Cancer Genes.

A central goal in cancer genomics is to identify the somatic alterations that underpin tumor initiation and progression. While commonly mutated cancer genes are readily identifiable, those that are rarely mutated across samples are difficult to distinguish from the large numbers of other infrequently mutated genes. We introduce a method, nCOP, that considers per-individual mutational profiles within the context of protein-protein interaction networks in order to identify small connected subnetworks of genes that, while not individually frequently mutated, comprise pathways that are altered across (i.e., "cover") a large fraction of individuals. By analyzing 6,038 samples across 24 different cancer types, we demonstrate that nCOP is highly effective in identifying cancer genes, including those with low mutation frequencies. Overall, our work demonstrates that combining per-individual mutational information with interaction networks is a powerful approach for tackling the mutational heterogeneity observed across cancers.

Searching for cancer vulnerabilities amid genetic chaos.

A meeting report on the Third European Association for Cancer Research Conference on Cancer Genomics, held at Churchill College, Cambridge, UK, 25-28 June 2017.

Genetic Progression of High Grade Prostatic Intraepithelial Neoplasia to Prostate Cancer.

BACKGROUND: Although high grade prostatic intraepithelial neoplasia (HGPIN) is considered a neoplastic lesion that precedes prostate cancer (PCA), the genomic structures of HGPIN remain unknown. OBJECTIVE: Identification of the genomic landscape of HGPIN and the genomic differences between HGPIN and PCA that may drive the progression to PCA. DESIGN, SETTINGS, AND PARTICIPANTS: We analyzed 20 regions of paired HGPIN and PCA from six patients using whole-exome sequencing and array-comparative genomic hybridization. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Somatic mutation and copy number alteration (CNA) profiles of paired HGPIN and PCA were measured and compared. RESULTS AND LIMITATIONS: The number of total mutations and CNAs of HGPINs were significantly fewer than those of PCAs. Mutations in FOXA1 and CNAs (1q and 8q gains) were detected in both HGPIN and PCA ('common'), suggesting their roles in early PCA development. Mutations in SPOP, KDM6A, and KMT2D were 'PCA-specific', suggesting their roles in HGPIN progression to PCA. The 8p loss was either 'common' or 'PCA-specific'. In-silico estimation of evolutionary ages predicted that HGPIN genomes were much younger than PCA genomes. Our data show that PCAs are direct descendants of HGPINs in most cases that require more genomic alterations to progress to PCA. The nature of heterogeneous HGPIN population that might attenuate genomic signals should further be studied. CONCLUSIONS: HGPIN genomes harbor relatively fewer mutations and CNAs than PCA but require additional hits for the progression. PATIENT SUMMARY: In this study, we suggest a systemic diagram from high grade prostatic intraepithelial neoplasia (HGPIN) to prostate cancer (PCA). Our results provide a clue to explain the long latency from HGPIN to PCA and provide useful information for the genetic diagnosis of HGPIN and PCA.

MUFFINN: cancer gene discovery via network analysis of somatic mutation data.

A major challenge for distinguishing cancer-causing driver mutations from inconsequential passenger mutations is the long-tail of infrequently mutated genes in cancer genomes. Here, we present and evaluate a method for prioritizing cancer genes accounting not only for mutations in individual genes but also in their neighbors in functional networks, MUFFINN (MUtations For Functional Impact on Network Neighbors). This pathway-centric method shows high sensitivity compared with gene-centric analyses of mutation data. Notably, only a marginal decrease in performance is observed when using 10 % of TCGA patient samples, suggesting the method may potentiate cancer genome projects with small patient populations.