Network of Cancer Genes: a web resource to analyze duplicability, orthology and network properties of cancer genes

Tri©DB: an integrated platform of knowledgebase and reporting system for cancer precision medicine

BACKGROUND

With the development of cancer precision medicine, a huge amount of high-dimensional cancer information has rapidly accumulated regarding gene alterations, diseases, therapeutic interventions and various annotations. The information is highly fragmented across multiple different sources, making it highly challenging to effectively utilize and exchange the information. Therefore, it is essential to create a resource platform containing well-aggregated, carefully mined, and easily accessible data for effective knowledge sharing.

METHODS

In this study, we have developed "Consensus Cancer Core" (Tri©DB), a new integrative cancer precision medicine knowledgebase and reporting system by mining and harmonizing multifaceted cancer data sources, and presenting them in a centralized platform with enhanced functionalities for accessibility, annotation and analysis.

RESULTS

The knowledgebase provides the currently most comprehensive information on cancer precision medicine covering more than 40 annotation entities, many of which are novel and have never been explored previously. Tri©DB offers several unique features: (i) harmonizing the cancer-related information from more than 30 data sources into one integrative platform for easy access; (ii) utilizing a variety of data analysis and graphical tools for enhanced user interaction with the high-dimensional data; (iii) containing a newly developed reporting system for automated annotation and therapy matching for external patient genomic data. Benchmark test indicated that Tri©DB is able to annotate 46% more treatments than two officially recognized resources, oncoKB and MCG. Tri©DB was further shown to have achieved 94.9% concordance with administered treatments in a real clinical trial.

CONCLUSIONS

The novel features and rich functionalities of the new platform will facilitate full access to cancer precision medicine data in one single platform and accommodate the needs of a broad range of researchers not only in translational medicine, but also in basic biomedical research. We believe that it will help to promote knowledge sharing in cancer precision medicine. Tri©DB is freely available at www.biomeddb.org , and is hosted on a cutting-edge technology architecture supporting all major browsers and mobile handsets.

ANKFN1 plays both protumorigenic and metastatic roles in hepatocellular carcinoma

Ankyrin repeat and fibronectin type III domain containing 1 (ANKFN1) is reported to be involved in human height and developmental abnormalities, but the expression profile and molecular function of ANKFN1 in hepatocellular carcinoma (HCC) remain unknown. This study aimed to evaluate the clinical significance and biological function of ANKFN1 in HCC and investigate whether ANKFN1 can be used for differential diagnosis in HCC. Here, we showed that ANKFN1 was upregulated in 126 tumor tissues compared with adjacent nontumorous tissues in HCC patients. The upregulation of ANKFN1 in HCC was associated with cirrhosis, alpha-fetoprotein (AFP) levels and poor prognosis. Moreover, silencing ANKFN1 expression suppressed HCC cell proliferation, migration, invasion, and metastasis in vitro and subcutaneous tumorigenesis in vivo. However, ANKFN1 overexpression promoted HCC proliferation and metastasis in an orthotopic liver transplantation model and attenuated the above biological effects in HCC cells. ANKFN1 significantly affected HCC cell proliferation by inducing G1/S transition and cell apoptosis. Mechanistically, we demonstrated that ANKFN1 promoted cell proliferation, migration, and invasion via activation of the cyclin D1/Cdk4/Cdk6 pathway by stimulating the MEK1/2-ERK1/2 pathway. Moreover, ANKFN1-induced cell proliferation, migration, and invasion were partially reversed by ERK1/2 inhibitors. Taken together, our results indicate that ANKFN1 promotes HCC cell proliferation and metastasis by activating the MEK1/2-ERK1/2 signaling pathway. Our work also suggests that ANKFN1 is a potential therapeutic target for HCC.

Comparative assessment of genes driving cancer and somatic evolution in non-cancer tissues: an update of the Network of Cancer Genes (NCG) resource

Background

Genetic alterations of somatic cells can drive non-malignant clone formation and promote cancer initiation. However, the link between these processes remains unclear and hampers our understanding of tissue homeostasis and cancer development.

Results

Here, we collect a literature-based repertoire of 3355 well-known or predicted drivers of cancer and non-cancer somatic evolution in 122 cancer types and 12 non-cancer tissues. Mapping the alterations of these genes in 7953 pan-cancer samples reveals that, despite the large size, the known compendium of drivers is still incomplete and biased towards frequently occurring coding mutations. High overlap exists between drivers of cancer and non-cancer somatic evolution, although significant differences emerge in their recurrence. We confirm and expand the unique properties of drivers and identify a core of evolutionarily conserved and essential genes whose germline variation is strongly counter-selected. Somatic alteration in even one of these genes is sufficient to drive clonal expansion but not malignant transformation.

Conclusions

Our study offers a comprehensive overview of our current understanding of the genetic events initiating clone expansion and cancer revealing significant gaps and biases that still need to be addressed. The compendium of cancer and non-cancer somatic drivers, their literature support, and properties are accessible in the Network of Cancer Genes and Healthy Drivers resource at http://www.network-cancer-genes.org/.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13059-022-02607-z.

C3: Consensus Cancer Driver Gene Caller

Next-generation sequencing has allowed identification of millions of somatic mutations in human cancer cells. A key challenge in interpreting cancer genomes is to distinguish drivers of cancer development among available genetic mutations. To address this issue, we present the first web-based application, consensus cancer driver gene caller (C³), to identify the consensus driver genes using six different complementary strategies, i.e., frequency-based, machine learning-based, functional bias-based, clustering-based, statistics model-based, and network-based strategies. This application allows users to specify customized operations when calling driver genes, and provides solid statistical evaluations and interpretable visualizations on the integration results. C³ is implemented in Python and is freely available for public use at http://drivergene.rwebox.com/c3.

The Network of Cancer Genes (NCG): a comprehensive catalogue of known and candidate cancer genes from cancer sequencing screens

The Network of Cancer Genes (NCG) is a manually curated repository of 2372 genes whose somatic modifications have known or predicted cancer driver roles. These genes were collected from 275 publications, including two sources of known cancer genes and 273 cancer sequencing screens of more than 100 cancer types from 34,905 cancer donors and multiple primary sites. This represents a more than 1.5-fold content increase compared to the previous version. NCG also annotates properties of cancer genes, such as duplicability, evolutionary origin, RNA and protein expression, miRNA and protein interactions, and protein function and essentiality. NCG is accessible at http://ncg.kcl.ac.uk/ .

NCG 5.0: updates of a manually curated repository of cancer genes and associated properties from cancer mutational screenings

The Network of Cancer Genes (NCG, http://ncg.kcl.ac.uk/) is a manually curated repository of cancer genes derived from the scientific literature. Due to the increasing amount of cancer genomic data, we have introduced a more robust procedure to extract cancer genes from published cancer mutational screenings and two curators independently reviewed each publication. NCG release 5.0 (August 2015) collects 1571 cancer genes from 175 published studies that describe 188 mutational screenings of 13 315 cancer samples from 49 cancer types and 24 primary sites. In addition to collecting cancer genes, NCG also provides information on the experimental validation that supports the role of these genes in cancer and annotates their properties (duplicability, evolutionary origin, expression profile, function and interactions with proteins and miRNAs).

Deep sequencing of the X chromosome reveals the proliferation history of colorectal adenomas

BACKGROUND

Mismatch repair deficient colorectal adenomas are composed of transformed cells that descend from a common founder and progressively accumulate genomic alterations. The proliferation history of these tumors is still largely unknown. Here we present a novel approach to rebuild the proliferation trees that recapitulate the history of individual colorectal adenomas by mapping the progressive acquisition of somatic point mutations during tumor growth.

RESULTS

Using our approach, we called high and low frequency mutations acquired in the X chromosome of four mismatch repair deficient colorectal adenomas deriving from male individuals. We clustered these mutations according to their frequencies and rebuilt the proliferation trees directly from the mutation clusters using a recursive algorithm. The trees of all four lesions were formed of a dominant subclone that co-existed with other genetically heterogeneous subpopulations of cells. However, despite this similar hierarchical organization, the growth dynamics varied among and within tumors, likely depending on a combination of tumor-specific genetic and environmental factors.

CONCLUSIONS

Our study provides insights into the biological properties of individual mismatch repair deficient colorectal adenomas that may influence their growth and also the response to therapy. Extended to other solid tumors, our novel approach could inform on the mechanisms of cancer progression and on the best treatment choice.

Two-layer modular analysis of gene and protein networks in breast cancer

Background

Genomic, proteomic and high-throughput gene expression data, when integrated, can be used to map the interaction networks between genes and proteins. Different approaches have been used to analyze these networks, especially in cancer, where mutations in biologically related genes that encode mutually interacting proteins are believed to be involved. This system of integrated networks as a whole exhibits emergent biological properties that are not obvious at the individual network level. We analyze the system in terms of modules, namely a set of densely interconnected nodes that can be further divided into submodules that are expected to participate in multiple biological activities in coordinated manner.

Results

In the present work we construct two layers of the breast cancer network: the gene layer, where the correlation network of breast cancer genes is analyzed to identify gene modules, and the protein layer, where each gene module is extended to map out the network of expressed proteins and their interactions in order to identify submodules. Each module and its associated submodules are analyzed to test the robustness of their topological distribution. The constituent biological phenomena are explored through the use of the Gene Ontology. We thus construct a “network of networks”, and demonstrate that both the gene and protein interaction networks are modular in nature. By focusing on the ontological classification, we are able to determine the entire GO profiles that are distributed at different levels of hierarchy. Within each submodule most of the proteins are biologically correlated, and participate in groups of distinct biological activities.

Conclusions

The present approach is an effective method for discovering coherent gene modules and protein submodules. We show that this also provides a means of determining biological pathways (both novel and as well those that have been reported previously) that are related, in the present instance, to breast cancer. Similar strategies are likely to be useful in the analysis of other diseases as well.

Single-Nucleotide Polymorphisms Within MicroRNAs Sequences and Their 3' UTR Target Sites May Regulate Gene Expression in Gastrointestinal Tract Cancers

Background:

Esophageal, stomach, and colorectal cancers are commonly lethal gastrointestinal tract (GIT) neoplasms, causing almost two million deaths worldwide each year. some environmental risk factors are acknowledged; however, genetic defects can significantly contribute to predisposition to GIT cancers. Accordingly, recent works have shown that single-nucleotide polymorphisms (SNPs) within miRNAs coding sequence (miR-SNPs) and miRNA target sites (target-SNPs) may further contribute to increased risk of developing cancer.

Objectives:

In this study, we comprehensively identified miRNA-target gene pairs implicated in GIT cancers and catalogued the presence of potentially functional miR-SNPs and target-SNPs that impair the correct functional recognition.

Materials and Methods:

Using bioinformatics tools, manual literature review, and a highly accurate dataset of experimentally validated miRNA-target gene interactions, we compiled a list of miRNA-target genes pairs related to GIT cancers and prioritized them into different groups based on the levels of experimental support. Functional annotations (gene ontology) were applied to these pairs in each group to gain further information.

Results:

We identified 97 pairs in which both miRNAs and target genes were implicated in GIT cancers. Several pairs, denoted as highly polymorphic pairs, had both miR-SNPs and target-SNPs. In addition, more than 5000 miRNA-target gene pairs were identified in which, according to the previous reports, either the miRNAs or the target genes had a direct involvement in GIT cancers. More than 800 target-SNPs are located in regulatory regions that were extracted from the ENCODE project through the RegulomeDB database. Of these, 20 were classified as expression quantitative trait loci (eQTLs).

Conclusions:

Our work provided a comprehensive source of prioritized and annotated candidate polymorphisms inside miRNAs and their target sites in GIT cancers, which would facilitate the process of choosing right candidate miRNA-target genes and related polymorphisms for future association or functional studies.

NCG 4.0: the network of cancer genes in the era of massive mutational screenings of cancer genomes

NCG 4.0 is the latest update of the Network of Cancer Genes, a web-based repository of systems-level properties of cancer genes. In its current version, the database collects information on 537 known (i.e. experimentally supported) and 1463 candidate (i.e. inferred using statistical methods) cancer genes. Candidate cancer genes derive from the manual revision of 67 original publications describing the mutational screening of 3460 human exomes and genomes in 23 different cancer types. For all 2000 cancer genes, duplicability, evolutionary origin, expression, functional annotation, interaction network with other human proteins and with microRNAs are reported. In addition to providing a substantial update of cancer-related information, NCG 4.0 also introduces two new features. The first is the annotation of possible false-positive cancer drivers, defined as candidate cancer genes inferred from large-scale screenings whose association with cancer is likely to be spurious. The second is the description of the systems-level properties of 64 human microRNAs that are causally involved in cancer progression (oncomiRs). Owing to the manual revision of all information, NCG 4.0 constitutes a complete and reliable resource on human coding and non-coding genes whose deregulation drives cancer onset and/or progression. NCG 4.0 can also be downloaded as a free application for Android smart phones. Database URL: http://bio.ieo.eu/ncg/.

Disease co-morbidity and the human Wnt signaling pathway: a network-wise study

The human Wnt signaling pathway contains 57 genes communicating among themselves by 70 experimentally established associations, as given in the KEGG/PATHWAY database. It is responsible for a variety of crucial biological functions such as regulation of cell fate determination, proliferation, differentiation, migration, and apoptosis. Abnormal behavior of its members causes numerous types of human cancers, dramatic changes in bone mass density that lead to diseases such as osteoporosis-pseudo-glioma syndrome, Van-Buchem disease, skeletal malformation, autosomal dominant sclerosteosis, and osteoporosis type I syndromes. So far, single genes have been investigated for their disease-causing properties, and single diseases have been traced backwards to discover foul-play of the system pathways. Differential expression of the whole genome has been mapped by microarray. But how all the genes involved in a pathway affect each other in single/multiple disease state(s) and whether the presence of one disease state makes a person prone to another kind of disease(s) (i.e., co-morbidity among diseases associated with a certain important biological pathway) is still unknown. We have developed a human Wnt signaling pathway diseasome and analyzed it for finding answers to such questions. Data used in constructing the diseasome can be downloaded from the publicly accessible webserver http://www.isical.ac.in/-rajat/diseasome/index.php.

Cellular hyperproliferation and cancer as evolutionary variables

Technological advances in biology have begun to dramatically change the way we think about evolution, development, health and disease. The ability to sequence the genomes of many individuals within a population, and across multiple species, has opened the door to the possibility of answering some long-standing and perplexing questions about our own genetic heritage. One such question revolves around the nature of cellular hyperproliferation. This cellular behavior is used to effect wound healing in most animals, as well as, in some animals, the regeneration of lost body parts. Yet at the same time, cellular hyperproliferation is the fundamental pathological condition responsible for cancers in humans. Here, I will discuss why microevolution, macroevolution and developmental biology all have to be taken into consideration when interpreting studies of both normal and malignant hyperproliferation. I will also illustrate how a synthesis of evolutionary sciences and developmental biology through the study of diverse model organisms can inform our understanding of both health and disease.

Cancer develops, progresses and responds to therapies through restricted perturbation of the protein-protein interaction network

The products of genes mutated or differentially expressed in cancer tend to occupy central positions within the network of protein-protein interactions, or the interactome network. Integration of different types of gene and protein relationships has considerably increased the understanding of the mechanisms of carcinogenesis, while also enhancing the applicability of expression signatures. In this scenario, however, it remains unknown how cancer develops, progresses and responds to therapies in a potentially controlled manner at the systems level. Here, by applying the concepts of load transfer and cascading failures in power grids, we examine the impact and transmission of cancer-related gene expression changes in the interactome network. Relative to random perturbations, this study reveals topological robustness associated with all cancer conditions. In addition, experimental perturbation of a central cancer node, which consists of over-expression of the α-synuclein (SNCA) protein in MCF7 breast cancer cells, also reveals robustness. Conversely, a search for proteins with an opposite topological impact identifies the autophagy pathway. Mechanistically, the existence of smaller shortest paths among cancer-related proteins appears to be a topological feature that partially contributes to the restricted perturbation of the network. Together, the results of this study suggest that cancer develops, progresses and responds to therapies following controlled, restricted perturbation of the interactome network.

Network of Cancer Genes (NCG 3.0): integration and analysis of genetic and network properties of cancer genes

The identification of a constantly increasing number of genes whose mutations are causally implicated in tumor initiation and progression (cancer genes) requires the development of tools to store and analyze them. The Network of Cancer Genes (NCG 3.0) collects information on 1494 cancer genes that have been found mutated in 16 different cancer types. These genes were collected from the Cancer Gene Census as well as from 18 whole exome and 11 whole-genome screenings of cancer samples. For each cancer gene, NCG 3.0 provides a summary of the gene features and the cross-reference to other databases. In addition, it describes duplicability, evolutionary origin, orthology, network properties, interaction partners, microRNA regulation and functional roles of cancer genes and of all genes that are related to them. This integrated network of information can be used to better characterize cancer genes in the context of the system in which they act. The data can also be used to identify novel candidates that share the same properties of known cancer genes and may therefore play a similar role in cancer. NCG 3.0 is freely available at http://bio.ifom-ieo-campus.it/ncg.

Human protein reference database and human proteinpedia as discovery resources for molecular biotechnology

In the recent years, research in molecular biotechnology has transformed from being small scale studies targeted at a single or a small set of molecule(s) into a combination of high throughput discovery platforms and extensive validations. Such a discovery platform provided an unbiased approach which resulted in the identification of several novel genetic and protein biomarkers. High throughput nature of these investigations coupled with higher sensitivity and specificity of Next Generation technologies provided qualitatively and quantitatively richer biological data. These developments have also revolutionized biological research and speed of data generation. However, it is becoming difficult for individual investigators to directly benefit from this data because they are not easily accessible. Data resources became necessary to assimilate, store and disseminate information that could allow future discoveries. We have developed two resources--Human Protein Reference Database (HPRD) and Human Proteinpedia, which integrate knowledge relevant to human proteins. A number of protein features including protein-protein interactions, post-translational modifications, subcellular localization, and tissue expression, which have been studied using different strategies were incorporated in these databases. Human Proteinpedia also provides a portal for community participation to annotate and share proteomic data and uses HPRD as the scaffold for data processing. Proteomic investigators can even share unpublished data in Human Proteinpedia, which provides a meaningful platform for data sharing. As proteomic information reflects a direct view of cellular systems, proteomics is expected to complement other areas of biology such as genomics, transcriptomics, molecular biology, cloning, and classical genetics in understanding the relationships among multiple facets of biological systems.

A genome-wide association study of DSM-IV cannabis dependence

Despite twin studies showing that 50-70% of variation in DSM-IV cannabis dependence is attributable to heritable influences, little is known of specific genotypes that influence vulnerability to cannabis dependence. We conducted a genome-wide association study of DSM-IV cannabis dependence. Association analyses of 708 DSM-IV cannabis-dependent cases with 2346 cannabis-exposed non-dependent controls was conducted using logistic regression in PLINK. None of the 948 142 single nucleotide polymorphisms met genome-wide significance (P at E-8). The lowest P values were obtained for polymorphisms on chromosome 17 (rs1019238 and rs1431318, P values at E-7) in the ANKFN1 gene. While replication is required, this study represents an important first step toward clarifying the biological underpinnings of cannabis dependence.

Integrative computational biology for cancer research

Over the past two decades, high-throughput (HTP) technologies such as microarrays and mass spectrometry have fundamentally changed clinical cancer research. They have revealed novel molecular markers of cancer subtypes, metastasis, and drug sensitivity and resistance. Some have been translated into the clinic as tools for early disease diagnosis, prognosis, and individualized treatment and response monitoring. Despite these successes, many challenges remain: HTP platforms are often noisy and suffer from false positives and false negatives; optimal analysis and successful validation require complex workflows; and great volumes of data are accumulating at a rapid pace. Here we discuss these challenges, and show how integrative computational biology can help diminish them by creating new software tools, analytical methods, and data standards.

Modification of gene duplicability during the evolution of protein interaction network

Duplications of genes encoding highly connected and essential proteins are selected against in several species but not in human, where duplicated genes encode highly connected proteins. To understand when and how gene duplicability changed in evolution, we compare gene and network properties in four species (Escherichia coli, yeast, fly, and human) that are representative of the increase in evolutionary complexity, defined as progressive growth in the number of genes, cells, and cell types. We find that the origin and conservation of a gene significantly correlates with the properties of the encoded protein in the protein-protein interaction network. All four species preserve a core of singleton and central hubs that originated early in evolution, are highly conserved, and accomplish basic biological functions. Another group of hubs appeared in metazoans and duplicated in vertebrates, mostly through vertebrate-specific whole genome duplication. Such recent and duplicated hubs are frequently targets of microRNAs and show tissue-selective expression, suggesting that these are alternative mechanisms to control their dosage. Our study shows how networks modified during evolution and contributes to explaining the occurrence of somatic genetic diseases, such as cancer, in terms of network perturbations.

DAnCER: disease-annotated chromatin epigenetics resource

Chromatin modification (CM) is a set of epigenetic processes that govern many aspects of DNA replication, transcription and repair. CM is carried out by groups of physically interacting proteins, and their disruption has been linked to a number of complex human diseases. CM remains largely unexplored, however, especially in higher eukaryotes such as human. Here we present the DAnCER resource, which integrates information on genes with CM function from five model organisms, including human. Currently integrated are gene functional annotations, Pfam domain architecture, protein interaction networks and associated human diseases. Additional supporting evidence includes orthology relationships across organisms, membership in protein complexes, and information on protein 3D structure. These data are available for 962 experimentally confirmed and manually curated CM genes and for over 5000 genes with predicted CM function on the basis of orthology and domain composition. DAnCER allows visual explorations of the integrated data and flexible query capabilities using a variety of data filters. In particular, disease information and functional annotations are mapped onto the protein interaction networks, enabling the user to formulate new hypotheses on the function and disease associations of a given gene based on those of its interaction partners. DAnCER is freely available at http://wodaklab.org/dancer/.

ALK inhibition for non-small cell lung cancer: from discovery to therapy in record time

It was only 3 years ago that an acquired translocation of EML4 with ALK leading to the expression of an EML4-ALK oncoprotein in non-small cell lung cancer (NSCLC) was reported. Tumor cells expressing EML4-ALK are "addicted" to its continued function. Now, crizotinib, an oral ALK inhibitor, is demonstrated to provide dramatic clinical benefit with little toxicity in patients having such advanced NSCLC, and a mechanism of clinical resistance to crizotinib is identified. Such therapy "targeted" at oncogenic proteins provides "personalized" medicine and prompts genome-wide mutation analysis of human tumors to find other therapeutic targets.

Translating tumor antigens into cancer vaccines

Vaccines represent a strategic successful tool used to prevent or contain diseases with high morbidity and/or mortality. However, while vaccines have proven to be effective in combating pathogenic microorganisms, based on the immune recognition of these foreign antigens, vaccines aimed at inducing effective antitumor activity are still unsatisfactory. Nevertheless, the effectiveness of the two licensed cancer-preventive vaccines targeting tumor-associated viral agents (anti-HBV [hepatitis B virus], to prevent HBV-associated hepatocellular carcinoma, and anti-HPV [human papillomavirus], to prevent HPV-associated cervical carcinoma), along with the recent FDA approval of sipuleucel-T (for the therapeutic treatment of prostate cancer), represents a significant advancement in the field of cancer vaccines and a boost for new studies in the field. Specific active immunotherapies based on anticancer vaccines represent, indeed, a field in continuous evolution and expansion. Significant improvements may result from the selection of the appropriate tumor-specific target antigen (to overcome the peripheral immune tolerance) and/or the development of immunization strategies effective at inducing a protective immune response. This review aims to describe the vast spectrum of tumor antigens and strategies to develop cancer vaccines.

The (r)evolution of cancer genetics

The identification of an increasing number of cancer genes is opening up unexpected scenarios in cancer genetics. When analyzed for their systemic properties, these genes show a general fragility towards perturbation. A recent paper published in BMC Biology shows how the founder domains of known cancer genes emerged at two macroevolutionary transitions - the advent of the first cell and the transition to metazoan multicellularity.

See research article http://www.biomedcentral.com/1741-7007/8/66

Phylostratigraphic tracking of cancer genes suggests a link to the emergence of multicellularity in metazoa

Background

Phylostratigraphy is a method used to correlate the evolutionary origin of founder genes (that is, functional founder protein domains) of gene families with particular macroevolutionary transitions. It is based on a model of genome evolution that suggests that the origin of complex phenotypic innovations will be accompanied by the emergence of such founder genes, the descendants of which can still be traced in extant organisms. The origin of multicellularity can be considered to be a macroevolutionary transition, for which new gene functions would have been required. Cancer should be tightly connected to multicellular life since it can be viewed as a malfunction of interaction between cells in a multicellular organism. A phylostratigraphic tracking of the origin of cancer genes should, therefore, also provide insights into the origin of multicellularity.

Results

We find two strong peaks of the emergence of cancer related protein domains, one at the time of the origin of the first cell and the other around the time of the evolution of the multicellular metazoan organisms. These peaks correlate with two major classes of cancer genes, the 'caretakers', which are involved in general functions that support genome stability and the 'gatekeepers', which are involved in cellular signalling and growth processes. Interestingly, this phylogenetic succession mirrors the ontogenetic succession of tumour progression, where mutations in caretakers are thought to precede mutations in gatekeepers.

Conclusions

A link between multicellularity and formation of cancer has often been predicted. However, this has not so far been explicitly tested. Although we find that a significant number of protein domains involved in cancer predate the origin of multicellularity, the second peak of cancer protein domain emergence is, indeed, connected to a phylogenetic level where multicellular animals have emerged. The fact that we can find a strong and consistent signal for this second peak in the phylostratigraphic map implies that a complex multi-level selection process has driven the transition to multicellularity.