oncogenomics oncogenomics oncogenomics is a sub-field of genomics that characterizes cancer-associated genes. it focuses on genomic, epigenomic and transcript alterations in cancer. cancer is a genetic disease caused by accumulation of dna mutations and epigenetic alterations leading to unrestrained cell proliferation and neoplasm formation. the goal of oncogenomics is to identify new oncogenes or tumor suppressor genes that may provide new insights into cancer diagnosis, predicting clinical outcome of cancers and new targets for cancer therapies. the success of targeted cancer therapies such as gleevec, herceptin and avastin raised the hope for oncogenomics to elucidate new targets for cancer treatment. overall goals of oncogenomics besides understanding the underlying genetic mechanisms that initiate or drive cancer progression, oncogenomics targets personalized cancer treatment. cancer develops due to dna mutations and epigenetic alterations that accumulate randomly. identifying and targeting the mutations in an individual patient may lead to increased treatment efficacy. the completion of the human genome project facilitated the field of oncogenomics and increased the abilities of researchers to find oncogenes. sequencing technologies and global methylation profiling techniques have been applied to the study of oncogenomics. history the genomics era began in the 1990s, with the generation of dna sequences of many organisms. in the 21st century, the completion of the human genome project enabled the study of functional genomics and examining tumor genomes. cancer is a main focus. the epigenomics era largely began more recently, about 2000. one major source of epigenetic change is altered methylation of cpg islands at the promoter region of genes (see dna methylation in cancer). a number of recently devised methods can assess the dna methylation status in cancers versus normal tissues. some methods assess methylation of cpgs located in different classes of loci, including cpg islands, shores, and shelves as well as promoters, gene bodies, and intergenic regions. cancer is also a major focus of epigenetic studies. access to whole cancer genome sequencing is important to cancer (or cancer genome) research because: mutations are the immediate cause of cancer and define the tumor phenotype. access to cancerous and normal tissue samples from the same patient and the fact that most cancer mutations represent somatic events, allow the identification of cancer-specific mutations. cancer mutations are cumulative and sometimes are related to disease stage. metastasis and drug resistance are distinguishable. access to methylation profiling is important to cancer research because: epi-drivers, along with mut-drivers, can act as immediate causes of cancers cancer epimutations are cumulative and sometimes related to disease stage whole genome sequencing the first cancer genome was sequenced in 2008. this study sequenced a typical acute myeloid leukaemia (aml) genome and its normal counterpart genome obtained from the same patient. the comparison revealed ten mutated genes. two were already thought to contribute to tumor progression: an internal tandem duplication of the flt3 receptor tyrosine kinase gene, which activates kinase signaling and is associated with a poor prognosis and a four base insertion in exon 12 of the npm1 gene (npmc). these mutations are found in 25-30% of aml tumors and are thought to contribute to disease progression rather than to cause it directly. the remaining 8 were new mutations and all were single base changes: four were in families that are strongly associated with cancer pathogenesis (ptprt, cdh24, pclkc and slc15a1). the other four had no previous association with cancer pathogenesis. they did have potential functions in metabolic pathways that suggested mechanisms by which they could act to promote cancer (kndc1, gpr124, eb12, grinc1b) these genes are involved in pathways known to contribute to cancer pathogenesis, but before this study most would not have been candidates for targeted gene therapy. this analysis validated the approach of whole cancer genome sequencing in identifying somatic mutations and the importance of parallel sequencing of normal and tumor cell genomes. in 2011, the genome of an exceptional bladder cancer patient whose tumor had been eliminated by the drug everolimus was sequenced, revealing mutations in two genes, tsc1 and nf2. the mutations disregulated mtor, the protein inhibited by everolimus, allowing it to reproduce without limit. as a result, in 2015, the exceptional responders initiative was created at the national cancer institute. the initiative allows such exceptional patients (who have responded positively for at least six months to a cancer drug that usually fails) to have their genomes sequenced to identify the relevant mutations. once identified, other patients could be screened for those mutations and then be given the drug. in 2016 to that end, a nationwide cancer drug trial began in 2015, involving up to twenty-four hundred centers. patients with appropriate mutations are matched with one of more than forty drugs. in 2014 the center for molecular oncology rolled out the msk-impact test, a screening tool that looks for mutations in 341 cancer-associated genes. by 2015 more than five thousand patients had been screened. patients with appropriate mutations are eligible to enroll in clinical trials that provide targeted therapy. technologies current technologies being used in oncogenomics.genomics technologies include: genome sequencing further information: cancer genome sequencing dna sequencing: pyrosequencing-based sequencers offer a relatively low-cost method to generate sequence data. array comparative genome hybridization: this technique measures the dna copy number differences between normal and cancer genomes. it uses the fluorescence intensity from fluorescent-labeled samples, which are hybridized to known probes on a microarray. representational oligonucleotide microarray analysis: detects copy number variation using amplified restriction-digested genomic fragments that are hybridized to human oligonucleotides, achieving a resolution between 30 and 35 kbit/s. digital karyotyping: detects copy number variation using genomics tags obtained via restriction enzyme digests. these tags are then linked to into ditags, concatenated, cloned, sequenced and mapped back to the reference genome to evaluate tag density. bacterial artificial chromosome (bac)-end sequencing (end-sequence profiling): identifies chromosomal breakpoints by generating a bac library from a cancer genome and sequencing their ends. the bac clones that contain chromosome aberrations have end sequences that do not map to a similar region of the reference genome, thus identifying a chromosomal breakpoint. transcriptomes microarrays: assess transcript abundance. useful in classification, prognosis, raise the possibility of differential treatment approaches and aid identification of mutations in the proteins' coding regions. the relative abundance of alternative transcripts has become an important feature of cancer research. particular alternative transcript forms correlate with specific cancer types. rna-seq bioinformatics and functional analysis of oncogenes bioinformatics technologies allow the statistical analysis of genomic data. the functional characteristics of oncogenes has yet to be established. potential functions include their transformational capabilities relating to tumour formation and specific roles at each stage of cancer development. after the detection of somatic cancer mutations across a cohort of cancer samples, bioinformatic computational analyses can be carried out to identify likely functional and likely driver mutations. there are three main approaches routinely used for this identification: mapping mutations, assessing the effect of mutation of the function of a protein or a regulatory element and finding signs of positive selection across a cohort of tumors. the approaches are not necessarily sequential however, there are important relationships of precedence between elements from the different approaches. different tools are used at each step. operomics operomics aims to integrate genomics, transcriptomics and proteomics to understand the molecular mechanisms that underlie the cancer development. comparative oncogenomics comparative oncogenomics uses cross-species comparisons to identify oncogenes. this research involves studying cancer genomes, transcriptomes and proteomes in model organisms such as mice, identifying potential oncogenes and referring back to human cancer samples to see whether homologues of these oncogenes are important in causing human cancers. genetic alterations in mouse models are similar to those found in human cancers. these models are generated by methods including retroviral insertion mutagenesis or graft transplantation of cancerous cells. source of cancer driver mutations, cancer mutagenesis mutations provide the raw material for natural selection in evolution and can be caused by errors of dna replication, the action of exogenous mutagens or endogenous dna damage. the machinery of replication and genome maintenance can be damaged by mutations, or altered by physiological conditions and differential levels of expression in cancer (see references in ). as pointed out by gao et al., the stability and integrity of the human genome are maintained by the dna-damage response (ddr) system. un-repaired dna damage is a major cause of mutations that drive carcinogenesis. if dna repair is deficient, dna damage tends to accumulate. such excess dna damage can increase mutational errors during dna replication due to error-prone translesion synthesis. excess dna damage can also increase epigenetic alterations due to errors during dna repair. such mutations and epigenetic alterations can give rise to cancer. ddr genes are often repressed in human cancer by epigenetic mechanisms. such repression may involve dna methylation of promoter regions or repression of ddr genes by a microrna. epigenetic repression of ddr genes occurs more frequently than gene mutation in many types of cancer (see cancer epigenetics). thus, epigenetic repression often plays a more important role than mutation in reducing expression of ddr genes. this reduced expression of ddr genes is likely an important driver of carcinogenesis. nucleotide sequence context influences mutation probability and analysis of mutational (mutable) dna motifs can be essential for understanding the mechanisms of mutagenesis in cancer. such motifs represent the fingerprints of interactions between dna and mutagens, between dna and repair/replication/modification enzymes. examples of motifs are the aid motif wrcy/rgyw (w = a or t, r = purine and y = pyrimidine) with c to t/g/a mutations, and error-prone dna pol η attributed aid-related mutations (a to g/c/g) in wa/tw motifs. another (agnostic) way to analyze the observed mutational spectra and dna sequence context of mutations in tumors involves pooling all mutations of different types and contexts from cancer samples into a discrete distribution. if multiple cancer samples are available, their context-dependent mutations can be represented in the form of a nonnegative matrix. this matrix can be further decomposed into components (mutational signatures) which ideally should describe individual mutagenic factors. several computational methods have been proposed for solving this decomposition problem. the first implementation of non-negative matrix factorization (nmf) method is available in sanger institute mutational signature framework in the form of a matlab package. on the other hand, if mutations from a single tumor sample are only available, the deconstructsigs r package and mutagene server may provide the identification of contributions of different mutational signatures for a single tumor sample. in addition, mutagene server provides mutagen or cancer-specific mutational background models and signatures that can be applied to calculate expected dna and protein site mutability to decouple relative contributions of mutagenesis and selection in carcinogenesis. synthetic lethality synthetic lethality arises when a combination of deficiencies in the expression of two or more genes leads to cell death, whereas a deficiency in only one of these genes does not. the deficiencies can arise through mutations, epigenetic alterations or inhibitors of one of the genes. the therapeutic potential of synthetic lethality as an efficacious anti-cancer strategy is continually improving. recently, the applicability of synthetic lethality to targeted cancer therapy has heightened due to the recent work of scientists including ronald a. depinho and colleagues, in what is termed 'collateral lethality'. muller et al. found that passenger genes, with chromosomal proximity to tumor suppressor genes, are collaterally deleted in some cancers. thus, the identification of collaterally deleted redundant genes carrying out an essential cellular function may be the untapped reservoir for then pursuing a synthetic lethality approach. collateral lethality therefore holds great potential in identification of novel and selective therapeutic targets in oncology. in 2012, muller et al. identified that homozygous deletion of redundant-essential glycolytic eno1 gene in human glioblastoma (gbm) is the consequence of proximity to 1p36 tumor suppressor locus deletions and may hold potential for a synthetic lethality approach to gbm inhibition. eno1 is one of three homologous genes (eno2, eno3) that encodes the mammalian alpha-enolase enzyme. eno2, which encodes enolase 2, is mostly expressed in neural tissues, leading to the postulation that in eno1-deleted gbm, eno2 may be the ideal target as the redundant homologue of eno1. muller found that both genetic and pharmacological eno2 inhibition in gbm cells with homozygous eno1 deletion elicits a synthetic lethality outcome by selective killing of gbm cells. in 2016, muller and colleagues discovered antibiotic sf2312 as a highly potent nanomolar-range enolase inhibitor which preferentially inhibits glioma cell proliferation and glycolytic