Signs of positive selection of somatic mutations in human cancers detected by EST sequence analysis

Antagonistic Pleiotropy in Human Disease

Between the 1930s and 1950s, scientists developed key principles of population genetics to try and explain the aging process. Almost a century later, these aging theories, including antagonistic pleiotropy and mutation accumulation, have been experimentally validated in animals. Although the theories have been much harder to test in humans despite research dating back to the 1970s, recent research is closing this evidence gap. Here we examine the strength of evidence for antagonistic pleiotropy in humans, one of the leading evolutionary explanations for the retention of genetic risk variation for non-communicable diseases. We discuss the analytical tools and types of data that are used to test for patterns of antagonistic pleiotropy and provide a primer of evolutionary theory on types of selection as a guide for understanding this mechanism and how it may manifest in other diseases. We find an abundance of non-experimental evidence for antagonistic pleiotropy in many diseases. In some cases, several studies have independently found corroborating evidence for this mechanism in the same or related sets of diseases including cancer and neurodegenerative diseases. Recent studies also suggest antagonistic pleiotropy may be involved in cardiovascular disease and diabetes. There are also compelling examples of disease risk variants that confer fitness benefits ranging from resistance to other diseases or survival in extreme environments. This provides increasingly strong support for the theory that antagonistic pleiotropic variants have enabled improved fitness but have been traded for higher burden of disease later in life. Future research in this field is required to better understand how this mechanism influences contemporary disease and possible consequences for their treatment.

Mutational signatures and mutable motifs in cancer genomes

Cancer is a genetic disorder, meaning that a plethora of different mutations, whether somatic or germ line, underlie the etiology of the 'Emperor of Maladies'. Point mutations, chromosomal rearrangements and copy number changes, whether they have occurred spontaneously in predisposed individuals or have been induced by intrinsic or extrinsic (environmental) mutagens, lead to the activation of oncogenes and inactivation of tumor suppressor genes, thereby promoting malignancy. This scenario has now been recognized and experimentally confirmed in a wide range of different contexts. Over the past decade, a surge in available sequencing technologies has allowed the sequencing of whole genomes from liquid malignancies and solid tumors belonging to different types and stages of cancer, giving birth to the new field of cancer genomics. One of the most striking discoveries has been that cancer genomes are highly enriched with mutations of specific kinds. It has been suggested that these mutations can be classified into 'families' based on their mutational signatures. A mutational signature may be regarded as a type of base substitution (e.g. C:G to T:A) within a particular context of neighboring nucleotide sequence (the bases upstream and/or downstream of the mutation). These mutational signatures, supplemented by mutable motifs (a wider mutational context), promise to help us to understand the nature of the mutational processes that operate during tumor evolution because they represent the footprints of interactions between DNA, mutagens and the enzymes of the repair/replication/modification pathways.

Signatures of positive selection reveal a universal role of chromatin modifiers as cancer driver genes

Tumors are composed of an evolving population of cells subjected to tissue-specific selection, which fuels tumor heterogeneity and ultimately complicates cancer driver gene identification. Here, we integrate cancer cell fraction, population recurrence, and functional impact of somatic mutations as signatures of selection into a Bayesian model for driver prediction. We demonstrate that our model, cDriver, outperforms competing methods when analyzing solid tumors, hematological malignancies, and pan-cancer datasets. Applying cDriver to exome sequencing data of 21 cancer types from 6,870 individuals revealed 98 unreported tumor type-driver gene connections. These novel connections are highly enriched for chromatin-modifying proteins, hinting at a universal role of chromatin regulation in cancer etiology. Although infrequently mutated as single genes, we show that chromatin modifiers are altered in a large fraction of cancer patients. In summary, we demonstrate that integration of evolutionary signatures is key for identifying mutational driver genes, thereby facilitating the discovery of novel therapeutic targets for cancer treatment.

Generalized portrait of cancer metabolic pathways inferred from a list of genes overexpressed in cancer

More than half a century from postulated Warburg theory of cancer cells origin, a question of changed metabolism in cancer is again taking the central place. Generalized picture of cancer metabolism was replaced by analysis of signaling and oncogenes in each type of cancer for several decades. However, now empowered with wealth of knowledge about tumor suppressors, oncogenes, and signaling pathways, reprogramming of cellular metabolism (e.g., increased glycolysis to respiration ratio in cancer cells) reemerged as an important element of cancer progression. To analyze level of expression of various proteins including metabolic enzymes across various cancers we used dbEST and Unigene data. We delineated a list of genes that are overexpressed in different types of cancer. We also grouped overexpressed enzymes into KEGG pathways and analyzed adjacent pathways to describe enzymatic reactions that take place in cancer cells and to identify major players that are abundant in cancer protein machinery. Glycolysis/gluconeogenesis and oxidative phosphorylation are the most abundant pathways although several other pathways are enriched in genes from our list. Ubiquitously overexpressed genes could be marked as nonspecific cancer-associated genes when analyzing genes that are overexpressed in certain types of cancer. Thus the list of overexpressed genes may be a useful tool for cancer research.

Building a statistical model for predicting cancer genes

More than 400 cancer genes have been identified in the human genome. The list is not yet complete. Statistical models predicting cancer genes may help with identification of novel cancer gene candidates. We used known prostate cancer (PCa) genes (identified through KnowledgeNet) as a training set to build a binary logistic regression model identifying PCa genes. Internal and external validation of the model was conducted using a validation set (also from KnowledgeNet), permutations, and external data on genes with recurrent prostate tumor mutations. We evaluated a set of 33 gene characteristics as predictors. Sixteen of the original 33 predictors were significant in the model. We found that a typical PCa gene is a prostate-specific transcription factor, kinase, or phosphatase with high interindividual variance of the expression level in adjacent normal prostate tissue and differential expression between normal prostate tissue and primary tumor. PCa genes are likely to have an antiapoptotic effect and to play a role in cell proliferation, angiogenesis, and cell adhesion. Their proteins are likely to be ubiquitinated or sumoylated but not acetylated. A number of novel PCa candidates have been proposed. Functional annotations of novel candidates identified antiapoptosis, regulation of cell proliferation, positive regulation of kinase activity, positive regulation of transferase activity, angiogenesis, positive regulation of cell division, and cell adhesion as top functions. We provide the list of the top 200 predicted PCa genes, which can be used as candidates for experimental validation. The model may be modified to predict genes for other cancer sites.

High-throughput resequencing of target-captured cDNA in cancer cells

The recent advent of whole exon (exome)-capture technology, coupled with second-generation sequencers, has made it possible to readily detect genomic alterations that affect encoded proteins in cancer cells. Such target resequencing of the cancer genome, however, fails to detect most clinically-relevant gene fusions, given that such oncogenic fusion genes are often generated through intron-to-intron ligation. To develop a resequencing platform that simultaneously captures point mutations, insertions-deletions (indels), and gene fusions in the cancer genome, we chose cDNA as the input for target capture and extensive resequencing, and we describe the versatility of such a cDNA-capture system. As a test case, we constructed a custom target-capture system for 913 cancer-related genes, and we purified cDNA fragments for the target gene set from five cell lines of CML. Our target gene set included Abelson murine leukemia viral oncogene homolog 1 (ABL1), but it did not include breakpoint cluster region (BCR); however, the sequence output faithfully detected reads spanning the fusion points of these two genes in all cell lines, confirming the ability of cDNA capture to detect gene fusions. Furthermore, computational analysis of the sequence dataset successfully identified non-synonymous mutations and indels, including those of tumor protein p53 (TP53). Our data might thus support the feasibility of a cDNA-capture system coupled with massively parallel sequencing as a simple platform for the detection of a variety of anomalies in protein-coding genes among hundreds of cancer specimens.

The possible evolutionary role of tumors in the origin of new cell types

The ability of tumor cells to differentiate in combination with their ability to express genes that are not expressed in normal tissues, may result in the emergence of new cell types in evolution. Tumors may play an evolutionary role by providing conditions (space and resources) for the expression of newly evolving genes. Genetically or epigenetically predetermined tumors at the early stages of progression, benign tumors, and some tumor-like processes in invertebrates and plants, all of which are modes of excess cell growth which provide evolving multicellular organisms with extra cell masses, are considered as potentially evolutionarily meaningful. Malignant tumors at the late stages of progression, however, are not. The preexisting cell types of multicellular organisms had restricted potential for the expression of newly evolving genes. Because of regulation and gene competition, some of the newly evolving genes may stay silent. Multicellular organisms would need excess cell masses for the expression of newly evolving genes. The preexisting cell types cannot provide such excess cell masses because of limitations imposed on the number of possible cell divisions. Tumors could provide the evolving multicellular organisms with the excess cell masses for the expression of newly evolving genes. We suggest that tumors could be a sort of proving ground (or reservoir) for the expression of newly evolving genes that originate in the course of genome evolution in the DNA of germ cells (i.e., not in tumor cells themselves). The case in which the expression of a newly evolving gene in tumors results in the origin of a new function would be associated with the origin of new feedback and regulatory circuits, as in root nodules in legumes and macromelanophores in Xiphophorus fishes. Tumor cells would differentiate, resulting in a new cell type for the given multicellular species. This cell type would be inherited because of epigenomic mechanisms similar to those in preexisting cell types. Populations of tumor-bearing organisms with genetically or epigenetically programmed tumors could represent the transition between established species of organisms at different stages of progressive evolution. Experimental confirmation of the prediction of the hypothesis of evolution by tumor cells differentiation concerning the expression of evolutionarily new genes and/or silent (neutrally evolving) sequences in tumor cells is presented.

Programmed genetic instability: a tumor-permissive mechanism for maintaining the evolvability of higher species through methylation-dependent mutation of DNA repair genes in the male germ line

Tumor suppressor genes are classified by their somatic behavior either as caretakers (CTs) that maintain DNA integrity or as gatekeepers (GKs) that regulate cell survival, but the germ line role of these disease-related gene subgroups may differ. To test this hypothesis, we have used genomic data mining to compare the features of human CTs (n = 38), GKs (n = 36), DNA repair genes (n = 165), apoptosis genes (n = 622), and their orthologs. This analysis reveals that repair genes are numerically less common than apoptosis genes in the genomes of multicellular organisms (P < 0.01), whereas CT orthologs are commoner than GK orthologs in unicellular organisms (P < 0.05). Gene targeting data show that CTs are less essential than GKs for survival of multicellular organisms (P < 0.0005) and that CT knockouts often permit offspring viability at the cost of male sterility. Patterns of human familial oncogenic mutations confirm that isolated CT loss is commoner than is isolated GK loss (P < 0.00001). In sexually reproducing species, CTs appear subject to less efficient purifying selection (i.e., higher Ka/Ks) than GKs (P = 0.000003); the faster evolution of CTs seems likely to be mediated by gene methylation and reduced transcription-coupled repair, based on differences in dinucleotide patterns (P = 0.001). These data suggest that germ line CT/repair gene function is relatively dispensable for survival, and imply that milder (e.g., epimutational) male prezygotic repair defects could enhance sperm variation—and hence environmental adaptation and speciation—while sparing fertility. We submit that CTs and repair genes are general targets for epigenetically initiated adaptive evolution, and propose a model in which human cancers arise in part as an evolutionarily programmed side effect of age- and damage-inducible genetic instability affecting both somatic and germ line lineages.

Distinguishing cancer-associated missense mutations from common polymorphisms

Missense variants are commonly identified in genomic sequence but only a small fraction directly contribute to oncogenesis. The ability to distinguish those missense changes that contribute to cancer progression from those that do not is a difficult problem usually only accomplished through functional in vivo analyses. Using two computational algorithms, Sorting Intolerant from Tolerant (SIFT) and the Pfam-based LogR.E-value method, we have identified features that distinguish cancer-associated missense mutations from other classes of missense change. Our data reveal that cancer mutants behave similarly to Mendelian disease mutations, but are clearly distinct from either complex disease mutations or common single-nucleotide polymorphisms. We show that both activating and inactivating oncogenic mutations are predicted to be deleterious, although activating changes are likely to increase protein activity. Using the Gene Ontology and data from the SIFT and LogR.E-value metrics, a classifier was built that predicts cancer-associated missense mutations with a very low false-positive rate. The classifier does remarkably well in a number of different experiments designed to distinguish polymorphisms from true cancer-associated mutations. We also show that recurrently observed mutations are much more likely to be predicted to be cancer-associated than rare mutations, suggesting that our classifier will be useful in distinguishing causal from passenger mutations. In addition, from an expressed sequence tag-based screen, we identified a previously unknown germ line change (P1104A) in tumor tissues that is predicted to disrupt the function of the TYK2 protein. The data presented here show that this novel bioinformatics approach to classifying cancer-associated variants is robust and can be used for large-scale analyses.