Yardena Samuels, Alberto Bardelli, Jared J. Gartner, and Carlos López-Otin
INTRODUCTION
There is a broad consensus that cancer is, in essence, a genetic disease, and that accumulation of molecular alterations in the genome of somatic cells is the basis of cancer progression (Fig. 1.1).1 In the past 10 years, the availability of the human genome sequence and progress in DNA sequencing technologies has dramatically improved knowledge of this disease. These new insights are transforming the field of oncology at multiple levels:
1. The genomic maps are redesigning the tumor taxonomy by moving it from a histologic- to a genetic-based level.
2. The success of cancer drugs designed to target the molecular alterations underlying tumorigenesis has proven that somatic genetic alterations are legitimate targets for therapy.
3. Tumor genotyping is helping clinicians individualize treatments by matching patients with the best treatment for their tumors.
4. Tumor-specific DNA alterations represent highly sensitive biomarkers for disease detection and monitoring.
5. Finally, the ongoing analyses of multiple cancer genomes will identify additional targets, whose pharmacologic exploitation will undoubtedly result in new therapeutic approaches.
This chapter will review the progress that has been made in understanding the genetic basis of sporadic cancers. An emphasis will be placed on an introduction to novel integrated genomic approaches that allow a comprehensive and systematic evaluation of genetic alterations that occur during the progression of cancer. Using these powerful tools, cancer research, diagnosis, and treatment are poised for a transformation in the next years.
CANCER GENES AND THEIR MUTATIONS
Cancer genes are broadly grouped into oncogenes and tumor suppressor genes. Using a classical analogy, oncogenes can be compared to a car accelerator, so that a mutation in an oncogene would be the equivalent of having the accelerator continuously pressed.2 Tumor suppressor genes, in contrast, act as brakes,2 so that when they are not mutated, they function to inhibit tumorigenesis. Oncogene and tumor suppressor genes may be classified by the nature of their somatic mutations in tumors. Mutations in oncogenes typically occur at specific hotspots, often affecting the same codon or clustered at neighboring codons in different tumors.1 Furthermore, mutations in oncogenes are almost always missense, and the mutations usually affect only one allele, making them heterozygous. In contrast, tumor suppressor genes are usually mutated throughout the gene; a large number of the mutations may truncate the encoded protein and generally affect both alleles, causing loss of heterozygosity (LOH). Major types of somatic mutations present in malignant tumors include nucleotide substitutions, small insertions and deletions (indels), chromosomal rearrangements, and copy number alterations.
IDENTIFICATION OF CANCER GENES
The completion of the Human Genome Project marked a new era in biomedical sciences.3 Knowledge of the sequence and organization of the human genome now allows for the systematic analysis of the genetic alterations underlying the origin and evolution of tumors. Before elucidation of the human genome, several cancer genes, such as KRAS, TP53, and APC, were successfully discovered using approaches based on an oncovirus analysis, linkage studies, LOH, and cytogenetics.4,5 The first curated version of the Human Genome Project was released in 2004,3 and provided a sequence-based map of the normal human genome. This information, together with the construction of the HapMap, which contains single nucleotide polymorphisms (SNP), and the underlying genomic structure of natural human genomic variation,6,7 allowed an extraordinary throughput in cataloging somatic mutations in cancer. These projects now offer an unprecedented opportunity: the identification of all the genetic changes associated with a human cancer. For the first time, this ambitious goal is within reach of the scientific community. Already, a number of studies have demonstrated the usefulness of strategies aimed at the systematic identification of somatic mutations associated with cancer progression. Notably, the Human Genome Project, the HapMap project, as well as the candidate and family gene approaches (described in the following paragraphs), utilized capillary-based DNA sequencing (first-generation sequencing, also known as Sanger sequencing).8 Figure 1.2 clearly illustrates the developments in the search of cancer genes, its increased pace, as well as the most relevant findings in this field.
Cancer Gene Discovery by Sequencing Candidate Gene Families
The availability of the human genome sequence provides new opportunities to comprehensively search for somatic mutations in cancer on a larger scale than previously possible. Progress in the field has been closely linked to improvements in the throughput of DNA analysis and in the continuous reduction in sequencing costs. What follows are some of the achievements in this research area, as well as how they affected knowledge of the cancer genome.
A seminal work in the field was the systematic mutational profiling of the genes involved in the RAS-RAF pathway in multiple tumors. This candidate gene approach led to the discovery that BRAF is frequently mutated in melanomas and is mutated at a lower frequency in other tumor types.9 Follow-up studies quickly revealed that mutations in BRAF are mutually exclusive with alterations in KRAS,9,10genetically emphasizing that these genes function in the same pathway, a concept that had been previously demonstrated in lower organisms such as Caenorhabditis elegans and Drosophila melanogaster.11,12
In 2003, the identification of cancer genes shifted from a candidate gene approach to the mutational analyses of gene families. The first gene families to be completely sequenced were those that involved protein13,14 and lipid phosphorylation.15 The rationale for initially focusing on these gene families was threefold:
The corresponding proteins were already known at that time to play a pivotal role in the signaling and proliferation of normal and cancerous cells.
Multiple members of the protein kinases family had already been linked to tumorigenesis.
Kinases are clearly amenable to pharmacologic inhibition, making them attractive drug targets.
The mutational analysis of all the tyrosine-kinase domains in colorectal cancers revealed that 30% of cases had a mutation in at least one tyrosine-kinase gene, and overall mutations were identified in eight different kinases, most of which had not previously been linked to cancer.13 An additional mutational analysis of the coding exons of 518 protein kinase genes in 210 diverse human cancers, including breast, lung, gastric, ovarian, renal, and acute lymphoblastic leukemia, identified approximately 120 mutated genes that probably contribute to oncogenesis.14 Because kinase activity is attenuated by enzymes that remove phosphate groups called phosphatases, the rational next step in these studies was to perform a mutation analysis of the protein tyrosine phosphatases. A mutational investigation of this family in colorectal cancer identified that 25% of cases had mutations in six different phosphatase genes (PTPRF, PTPRG, PTPRT, PTPN3, PTPN13, or PTPN14).16 A combined analysis of the protein tyrosine kinases and the protein tyrosine phosphatases showed that 50% of colorectal cancers had mutations in a tyrosine-kinase gene, a protein tyrosine phosphatase gene, or both, further emphasizing the pivotal role of protein phosphorylation in neoplastic progression. Many of the identified genes had previously been linked to human cancer, thus validating the unbiased comprehensive mutation profiling. These landmark studies led to additional gene family surveys.
The phosphatidylinositol 3-kinase (PI3K) gene family, which also plays a role in proliferation, adhesion, survival, and motility, was also comprehensively investigated.17 Sequencing of the exons encoding the kinase domain of all 16 members belonging to this family pinpointed PIK3CA as the only gene to harbor somatic mutations. When the entire coding region was analyzed, PIK3CA was found to be somatically mutated in 32% of colorectal cancers. At that time, the PIK3CA gene was certainly not a newcomer in the cancer arena, because it had previously been shown to be involved in cell transformation and metastasis.17 Strikingly, its staggeringly high mutation frequency was discovered only through systematic sequencing of the corresponding gene family.15 Subsequent analysis of PIK3CA in other tumor types identified somatic mutations in this gene in additional cancer types, including 36% of hepatocellular carcinomas, 36% of endometrial carcinomas, 25% of breast carcinomas, 15% of anaplastic oligodendrogliomas, 5% of medulloblastomas and anaplastic astrocytomas, and 27% of glioblastomas.18–22 It is known that PIK3CA is one of the two (the other being KRAS) most commonly mutated oncogenes in human cancers. Further investigation of the PI3K pathway in colorectal cancer showed that 40% of tumors had genetic alterations in one of the PI3K pathway genes, emphasizing the central role of this pathway in colorectal cancer pathogenesis.23
Although most cancer genome studies of large gene families have focused on the kinome, recent analyses have revealed that members of other families highly represented in the human genome are also a target of mutational events in cancer. This is the case of proteases, a complex group of enzymes consisting of at least 569 components that constitute the so-called human degradome.24 Proteases exhibit an elaborate interplay with kinases and have traditionally been associated with cancer progression because of their ability to degrade extracellular matrices, thus facilitating tumor invasion and metastasis.25,26However, recent studies have shown that these enzymes hydrolyze a wide variety of substrates and influence many different steps of cancer, including early stages of tumor evolution.27 These functional studies have also revealed that beyond their initial recognition as prometastatic enzymes, they play dual roles in cancer, as assessed by the identification of a growing number of tumor-suppressive proteases.28
These findings emphasized the possibility that mutational activation or inactivation of protease genes occurs in cancer. A systematic analysis of genetic alterations in breast and colorectal cancers revealed that proteases from different catalytic classes were somatically mutated in cancer.29 These results prompted the mutational analysis of entire protease families such as matrix metalloproteinases (MMP), a disintegrin and metalloproteinase (ADAM), and ADAMs with thrombospondin domains (ADAMTS) in different tumors. These studies led to the identification of protease genes frequently mutated in cancer, such as MMP8, which is mutated and functionally inactivated in 6.3% of human melanomas.30,31
The mutational status of caspases has also been extensively analyzed in different tumors because these proteases play a fundamental role in the execution of apoptosis, one of the hallmarks of cancer.32 These studies demonstrated that CASP8 is deleted in neuroblastomas and inactivated by somatic mutations in a variety of human malignancies, including head and neck, colorectal, lung, and gastric carcinomas.33–35Other large protease families whose components are often mutated in cancer are the deubiquitinating enzymes (DUB), which catalyze the removal of ubiquitin and ubiquitin-like modifiers of their target proteins.36 Some DUBs were initially identified as oncogenic proteins, but further work has shown that other deubiquitinases, such as CYLD, A20, and BAP1, are tumor suppressors inactivated in cancer. CYLD is mutated in patients with familial cylindromatosis, a disease characterized by the formation of multiple tumors of skin appendages.37 A20 is a DUB family member encoded by the TNFAIP3 gene, which is mutated in a large number of Hodgkin lymphomas and primary mediastinal B-cell lymphomas.38–41 Finally, the BAP1 gene, encoding an ubiquitin C-terminal hydrolase, is frequently mutated in metastasizing uveal melanomas42 and in other human malignancies, such as mesothelioma and renal cell carcinoma.43
Mutational Analysis of Exomes Using Sanger Sequencing
Although the gene family approach for the identification of cancer genes has proven extremely valuable, it still is a candidate approach and thus biased in its nature. The next step forward in the mutational profiling of cancer has been the sequencing of exomes, which is the entire coding portion of the human genome (18,000 protein-encoding genes). The exomes of many different tumors—including breast, colorectal, pancreatic, and ovarian clear cell carcinomas; glioblastoma multiforme; and medulloblastoma—have been analyzed using Sanger sequencing. For the first time, these large-scale analyses allowed researchers to describe and understand the genetic complexity of human cancers.29,44–48 The declared goals of these exome studies were to provide methods for exomewide mutational analyses in human tumors, to characterize their spectrum and quantity of somatic mutations, and, finally, to discover new genes involved in tumorigenesis as well as novel pathways that have a role in these tumors. In these studies, sequencing data were complemented with gene expression and copy number analyses, thus providing a comprehensive view of the genetic complexity of human tumors.45–48 A number of conclusions can be drawn from these analyses, including the following:
Cancer genomes have an average of 30 to 100 somatic alterations per tumor in coding regions, which was a higher number than previously thought. Although the alterations included point mutations, small insertions, deletions, or amplifications, the great majority of the mutations observed were single-base substitutions.45,46
Even within a single cancer type, there is a significant intertumor heterogeneity. This means that multiple mutational patterns (encompassing different mutant genes) are present in tumors that cannot be distinguished based on histologic analysis. The concept that individual tumors have a unique genetic milieu is highly relevant for personalized medicine, a concept that will be further discussed.
The spectrum and nucleotide contexts of mutations differ between different tumor types. For example, over 50% of mutations in colorectal cancer were C:G to T:A transitions, and 10% were C:G to G:C transversions. In contrast, in breast cancers, only 35% of the mutations were C:G to T:A transitions, and 29% were C:G to G:C transversions. Knowledge of mutation spectra is vital because it allows insight into the mechanisms underlying mutagenesis and repair in the various cancers investigated.
A considerably larger number of genes that had not been previously reported to be involved in cancer were found to play a role in the disease.
Solid tumors arising in children, such as medulloblastomas, harbor on average 5 to 10 times less gene alterations compared to a typical adult solid tumor. These pediatric tumors also harbor fewer amplifications and homozygous deletions within coding genes compared to adult solid tumors.
Importantly, to deal with the large amount of data generated in these genomic projects, it was necessary to develop new statistical and bioinformatic tools. Furthermore, an examination of the overall distribution of the identified mutations allowed for the development of a novel view of cancer genome landscapes and a novel definition of cancer genes. These new concepts in the understanding of cancer genetics are further discussed in the following paragraphs. The compiled conclusions derived from these analyses have led to a paradigm shift in the understanding of cancer genetics.
A clear indication of the power of the unbiased nature of the whole exome surveys was revealed by the discovery of recurrent mutations in the active site of IDH1, a gene with no known link to gliomas, in 12% of tumors analyzed.46 Because malignant gliomas are the most common and lethal tumors of the central nervous system, and because glioblastoma multiforme (GBM; World Health Organization grade IV astrocytoma) is the most biologically aggressive subtype, the unveiling of IDH1 as a novel GBM gene is extremely significant. Importantly, mutations of IDH1 predominantly occurred in younger patients and were associated with a better prognosis.49 Follow-up studies showed that mutations of IDH1 occur early in glioma progression; the R132 somatic mutation is harbored by the majority (greater than 70%) of grades II and III astrocytomas and oligodendrogliomas, as well as in secondary GBMs that develop from these lower grade lesions.49–55 In contrast, less than 10% of primary GBMs harbor these alterations. Furthermore, analysis of the associated IDH2 revealed recurrent somatic mutations in the R172 residue, which is the exact analog of the frequently mutated R132 residue of IDH1. These mutations occur mostly in a mutually exclusive manner with IDH1 mutations,49,51 suggesting that they have equivalent phenotypic effects. Subsequently, IDH1mutations have been reported in additional cancer types, including hematologic neoplasias.56–58
Next-Generation Sequencing and Cancer Genome Analysis
In 1977, the introduction of the Sanger method for DNA sequencing with chain-terminating inhibitors transformed biomedical research.8 Over the past 30 years, this first-generation technology has been universally used for elucidating the nucleotide sequence of DNA molecules. However, the launching of new large-scale projects, including those implicating whole-genome sequencing of cancer samples, has made necessary the development of new methods that are widely known as next-generation sequencing technologies.59–61 These approaches have significantly lowered the cost and the time required to determine the sequence of the 3 × 109 nucleotides present in the human genome. Moreover, they have a series of advantages over Sanger sequencing, which are of special interest for the analysis of cancer genomes.62 First, next-generation sequencing approaches are more sensitive than Sanger methods and can detect somatic mutations even when they are present in only a subset of tumor cells.63 Moreover, these new sequencing strategies are quantitative and can be used to simultaneously determine both nucleotide sequence and copy number variations.64 They can also be coupled to other procedures such as those involving paired-end reads, allowing for the identification of multiple structural alterations, such as insertions, deletions, and rearrangements, that commonly occur in cancer genomes.63 Nonetheless, next-generation sequencing still presents some limitations that are mainly derived from the relatively high error rate in the short reads generated during the sequencing process. In addition, these short reads make the task of de novo assembly of the generated sequences and the mapping of the reads to a reference genome extremely complex. To overcome some of these current limitations, deep coverage of each analyzed genome is required and a careful validation of the identified variants must be performed, typically using Sanger sequencing. As a consequence, there is a substantial increase in both the cost of the process and in the time of analysis. Therefore, it can be concluded that whole-genome sequencing of cancer samples is already a feasible task, but not yet a routine process. Further technical improvements will be required before the task of decoding the entire genome of any malignant tumor of any cancer patient can be applied to clinical practice.
The number of next-generation sequencing platforms has substantially grown over the past few years and currently includes technologies from Roche/454, Illumina/Solexa, Life/APG’s SOLiD3, Helicos BioSciences/HeliScope, and Pacific Biosciences/PacBio RS.61 Noteworthy also are the recent introduction of the Polonator G.007 instrument, an open source platform with freely available software and protocols; the Ion Torrent’s semiconductor sequencer; as well as those involving self-assembling DNA nanoballs or nanopore technologies.65–67 These new machines are driving the field toward the era of third-generation sequencing, which brings enormous clinical interest because it can substantially increase the speed and accuracy of analyses at reduced costs and can facilitate the possibility of single-molecule sequencing of human genomes. A comparison of next-generation sequencing platforms is shown in Table 1.1. These various platforms differ in the method utilized for template preparation and in the nucleotide sequencing and imaging strategy, which finally result in their different performance. Ultimately, the most suitable approach depends on the specific genome sequencing projects.61
Current methods of template preparation first involve randomly shearing genomic DNA into smaller fragments, from which a library of either fragment templates or mate-pair templates are generated. Then, clonally amplified templates from single DNA molecules are prepared by either emulsion polymerase chain reaction (PCR) or solid-phase amplification.68,69 Alternatively, it is possible to prepare single-molecule templates through methods that require less starting material and that do not involve PCR amplification reactions, which can be the source of artifactual mutations.70 Once prepared, templates are attached to a solid surface in spatially separated sites, allowing thousands to billions of nucleotide sequencing reactions to be performed simultaneously.
The sequencing methods currently used by the different next-generation sequencing platforms are diverse and have been classified into four groups: cyclic reversible termination, single-nucleotide addition, real-time sequencing, and sequencing by ligation (Fig. 1.3).61,71 These sequencing strategies are coupled with different imaging methods, including those based on measuring bioluminescent signals or involving four-color imaging of single molecular events. Finally, the extraordinary amount of data released from these nucleotide sequencing platforms is stored, assembled, and analyzed using powerful bioinformatic tools that have been developed in parallel with next-generation sequencing technologies.72
Next-generation sequencing approaches represent the newest entry into the cancer genome decoding arena and have already been applied to cancer analyses. The first research group to apply these methodologies to whole cancer genomes was that of Ley et al.,73 who reported in 2008 the sequencing of the entire genome of a patient with acute myeloid leukemia (AML) and its comparison with the normal tissue from the same patient, using the Illumina/Solexa platform. As further described, this work allowed for the identification of point mutations and structural alterations of putative oncogenic relevance in AML and represented proof of principle of the relevance of next-generation sequencing for cancer research.
Whole-Genome Analysis Utilizing Second-Generation Sequencing
The sequence of the first whole cancer genome was reported in 2008, where AML and normal skin from the same patient were described.73 Numerous additional whole genomes, together with the corresponding normal genomes of patients with a variety of malignant tumors, have been reported since then.56,63,74–86
The first available whole genome of a cytogenetically normal AML subtype M1 (AML-M1) revealed eight genes with novel mutations along with another 500 to 1,000 additional mutations found in noncoding regions of the genome. Most of the identified genes had not been previously associated with cancer. However, validation of the detected mutations did not identify novel recurring mutations in AML.73 Concomitantly, with the expansion in the use of next-generation sequencers, many other whole genomes from a number of cancer types started to be evaluated in a similar manner (Fig. 1.4).87
In contrast to the first AML whole genome, the second did observe a recurrent mutation in IDH1, encoding isocitrate dehydrogenase.56 Follow-up studies extended this finding and reported that mutations in IDH1 and the related gene IDH2 occur at a 20% to 30% frequency in AML patients and are associated with a poor prognosis in some subgroups of patients.79,80,88 A good example illustrating the high pace at which second-generation technologies and their accompanying analytical tools are found is demonstrated by the following finding derived from a reanalysis of the first AML whole genome. Thus, when improvements in sequencing techniques were available, the first AML whole genome (described previously), which identified no recurring mutations and had a 91.2% diploid coverage, was reevaluated by deeper sequence coverage, yielding 99.6% diploid coverage of the genome. This improvement, together with more advanced mutation calling algorithms, allowed for the discovery of several nonsynonymous mutations that had not been identified in the initial sequencing. This included a frameshift mutation in the DNA methyltransferase gene DNMT3A. Validation of DNMT3A in 280 additional de novo AML patients to define recurring mutations led to the significant discovery that a total of 22.1% of AML cases had mutations in DNMT3A that were predicted to affect translation. The median overall survival among patients with DNMT3A mutations was significantly shorter than that among patients without such mutations (12.3 months versus 41.1 months; p <0.001).
Shortly after this study, complete sequences of a series of cancer genomes, together with matched normal genomes of the same patients, were reported.56,78,83,84 These works opened the way to more ambitious initiatives, including those involving large international consortia, aimed at decoding the genome of malignant tumors from thousands of cancer patients. Thus, over the last 2 years, many whole genomes of different human malignancies have been made available.74–76
In addition to direct applications of next-generation sequencing technologies for the mutational analysis of cancer genomes, these methods have an additional range of applications in cancer research. Thus, genome sequencing efforts have begun to elucidate the genomic changes that accompany metastasis evolution through a comparative analysis of primary and metastatic lesions from breast and pancreatic cancer patients.77,81,82,85 Likewise, massively parallel sequencing has been used to analyze the evolution of a tongue adenocarcinoma in response to selection by targeted kinase inhibitors.89 Detailed information of several of these whole genome projects is found in the following paragraph.
The first solid cancer to undergo whole-genome sequencing was a malignant melanoma that was compared to a lymphoblastoid cell line from the same individual.83 Impressively, a total of 33,345 somatic base substitutions were identified, with 187 nonsynonymous substitutions in protein-coding sequences, at least one order of magnitude higher than any other cancer type. Most somatic base substitutions were C:G > T:A transitions, and of the 510 dinucleotide substitutions, 360 were CC.TT/GG.AA changes, which is consistent with ultraviolet light exposure mutation signatures previously reported in melanoma.14 Such results from the most comprehensive catalog of somatic mutations not only provide insight into the DNA damage signature in this cancer type, but can also be useful in determining the relative order of some acquired mutations. Indeed, this study shows that a significant correlation exists between the presence of a higher proportion of C.A/G.T transitions in early (82%) compared to late mutations (53%). Another important aspect that the comprehensive nature of this melanoma study provided was that cancer mutations are spread out unevenly throughout the genome, with a lower prevalence in regions of transcribed genes, suggesting that DNA repair occurs mainly in these areas.
An interesting and pioneering example of the power of whole-genome sequencing in deciphering the mutation evolution in carcinogenesis was seen in a study in which a basallike breast cancer tumor, a brain metastasis, a tumor xenograft derived from the primary tumor, and the peripheral blood from the same patient were compared (Fig. 1.5).85 This analysis showed a wide range of mutant allele frequencies in the primary tumor, which was narrowed in the metastasis and xenograft samples. This suggested that the primary tumor was significantly more heterogeneous in its cell populations compared to its matched metastasis and xenograft samples because these underwent selection processes whether during metastasis or transplantation. The clear overlap in mutation incidence between the metastatic and xenograft cases suggests that xenografts undergo similar selection as metastatic lesions and, therefore, are a reliable source for genomic analyses. The main conclusion of this whole-genome study was that, although metastatic tumors harbor an increased number of genetic alterations, the majority of the alterations found in the primary tumor are preserved. Interestingly, single-cell genome sequencing of a breast primary tumour and its liver metastasis indicated that a single clonal expansion formed the primary tumor and seeded the metastasis.90 Further studies have confirmed and extended these findings to metastatic tumors from different types, including renal and pancreatic carcinomas.91
The importance of performing whole-genome sequencing has also been emphasized by the recent identification of somatic mutations in regulatory regions, which can also elicit tumorigenesis. In a study reviewing the noncoding mutations in 19 melanoma whole-genome samples, two recurrent mutations in 17 of the 19 cases studied within the telomerase reverse transcriptase (TERT) promoter region were revealed.92 When these two mutations were investigated in an extension of 51 additional tumors and their matched normal tissues, it was observed that 33 tumors harbored one of the mutations and that the mutations occurred in a mutually exclusive manner. These two mutations generate an identical 11 bp nucleotide stretch that contains the consensus binding site for E-twenty-six (ETS) transcription factors. When cloned into a luciferase reporter assay system, it was shown that these mutations conferred a two- to fourfold increase in transcriptional activity of this promoter in five melanoma cell lines. Although this alteration is much more frequent in melanoma, it is also present in other cancer types because 16% of the cancers listed in the Cancer Cell Line Encyclopedia harbor one of the two TERT mutations. In combination, these TERT mutations are seen in a greater frequency than BRAF- and NRAS-activating mutations. They occur in a mutually exclusive manner and in regions that do not show a large background mutation rate, all suggesting that these mutations are important driver events contributing to oncogenesis. Further supporting this was another recent study that identified these same two mutations in the germ line of familial melanoma patients.93
As the TERT promoter mutation discovery shows, regions of the genome that do not code for proteins are just as vital in our understanding of the biology behind tumor development and progression. Another class of non–protein-coding regions in the genome are the noncoding RNAs. One class of noncoding RNAs are microRNAs (miRNA). Discovered 20 years ago, miRNAs are known to be expressed in a tissue or developmentally specific manner and their expression can influence cellular growth and differentiation along with cancer-related pathways such as apoptosis or stress response. miRNAs do this through either overexpression, leading to the targeting and downregulation of tumor suppressor genes, or inversely through their own downregulation, leading to increased expression of their target oncogene. miRNAs have been extensively studied in cancer and their functional effects have been noted in a wide variety of cancers like glioma94 and breast cancer,95 to name just a few.
Another class of noncoding RNAs (ncRNA) are the long noncoding RNAs (lncRNA). These RNAs are typically greater than 200 bp and can range up to 100 kb in size. They are transcribed by RNA polymerase II and can undergo splicing and polyadenylation. Although much less extensively studied when compared to miRNAs for their role in cancer, lncRNAs are beginning to come under much more scrutiny. A recent study of the steroid receptor RNA activator (SRA) revealed two transcripts, a lncRNA (SRA) and a translated transcript (steroid receptor RNA activator protein [SRAP]), that coexist within breast cancer cells. However, their expression varies within breast cancer cell lines with different phenotypes. It was shown that in a more invasive breast cancer line, higher relative levels of the noncoding transcript were seen.96 Because this ncRNA acts as part of a ribonucleoprotein complex that is recruited to the promoter region of regulatory genes, it has been hypothesized that this shift in balance between both noncoding and coding transcripts may be associated with growth advantages. When this balance was shifted in vitro, it led to a large increase in transcripts associated with invasion and migration. The results of this study highlight the importance of the investigation into the roles of ncRNA in tumor development or progression and confirm again that the study of coding variants is not sufficient in determining the full genomic spectrum of cancer.
It must be also noted that the recent analysis of whole genomes of many different human tumors has provided additional insights into cancer evolution. Thus, it has been demonstrated that multiple mutational processes are operative during cancer development and progression, each of which has the capacity to leave its particular mutational signature on the genome. A remarkable and innovative study in this regard was aimed at the generation of the entire catalog of somatic mutations in 21 breast carcinomas and the identification of the mutational signatures of the underlying processes. This analysis revealed the occurrence of multiple, distinct single- and double-nucleotide substitution signatures. Moreover, it was reported that breast carcinomas harboring BRCA1 or BRCA2 mutations showed a characteristic combination of substitution mutation signatures and a particular profile of genomic deletions. An additional contribution of this analysis was the identification of a distinctive phenomenon of localized hypermutation, which has been termed kataegis, and which has also subsequently been observed in other malignancies distinct from breast carcinomas.87
Whole-genome sequencing of human carcinomas has also allowed for the ability to characterize other massive genomic alterations, termed chromothripsis and chromoplexy, occurring across different cancer subtypes.97Chromothripsis implies a massive genomic rearrangement acquired in a one-step catastrophic event during cancer development and has been detected in about 2% to 3% of all tumors, but is present at high frequency in some particular cases, such as bone cancers.98 Chromoplexy has been originally described in prostate cancer and involves many DNA translocations and deletions that arise in a highly interdependent manner and result in the coordinate disruption of multiple cancer genes.99 These newly described phenomena represent powerful strategies of rapid genome evolution, which may play essential roles during carcinogenesis.
Whole-Exome Analysis Utilizing Second-Generation Sequencing
Another application of second-generation sequencing involves utilizing nucleic acid “baits” to capture regions of interest in the total pool of nucleic acids. These could either be DNA, as described previously,100,101 or RNA.102 Indeed, most areas of interest in the genome can be targeted, including exons and ncRNAs. Despite inefficiencies in the exome-targeting process—including the uneven capture efficiency across exons, which results in not all exons being sequenced, and the occurrence of some off-target hybridization events—the higher coverage of the exome makes it highly suitable for mutation discovery in cancer samples.
Over the last few years, thousands of cancer samples have been subjected to whole-exome sequencing. These studies, combined with data from whole-genome sequencing, have provided an unprecedented level of information about the mutational landscape of the most frequent human malignancies.74–76 In addition, whole-exome sequencing has been used to identify the somatic mutations characteristic of both rare tumors and those that are prevalent in certain geographical regions.76
Overall, these studies have provided very valuable information about mutation rates and spectra across cancer types and subtypes.87,103,104 Remarkably, the variation in mutational frequency between different tumors is extraordinary, with hematologic and pediatric cancers showing the lowest mutation rates (0.001 per Mb of DNA), and melanoma and lung cancers presenting the highest mutational burden (more than 400 per Mb). Whole-exome sequencing has also contributed to the identification of novel cancer genes that had not been previously described to be causally implicated in the carcinogenesis process. These genes belong to different functional categories, including signal transduction, RNA maturation, metabolic regulation, epigenetics, chromatin remodeling, and protein homeostasis.74 Finally, a combination of data from whole-exome and whole-genome sequencing has allowed for the identification of the signatures of mutational processes operating in different cancer types.87 Thus, an analysis of a dataset of about 5 million mutations from over 7,000 cancers from 30 different types has allowed for the extraction of more than 20 distinct mutational signatures. Some of them, such as those derived from the activity of APOBEC cytidine deaminases, are present in most cancer types, whereas others are characteristic of specific tumors. Known signatures associated with age, smoking, ultraviolet (UV) light exposure, and DNA repair defects have been also identified in this work, but many of the detected mutational signatures are of cryptic origin. These findings demonstrate the impressive diversity of mutational processes underlying cancer development and may have enormous implications for the future understanding of cancer biology, prevention, and treatment.
SOMATIC ALTERATION CLASSES DETECTED BY CANCER GENOME ANALYSIS
Whole-genome sequencing of cancer genomes has an enormous potential to detect all major types of somatic mutations present in malignant tumors. This large repertoire of genomic abnormalities includes single nucleotide changes, small insertions and deletions, large chromosomal reorganizations, and copy number variations (Fig. 1.6).
Nucleotide substitutions are the most frequent somatic mutations detected in malignant tumors, although there is a substantial variability in the mutational frequency among different cancers.60 On average, human malignancies have one nucleotide change per million bases, but melanomas reach mutational rates 10-fold higher, and tumors with mutator phenotype caused by DNA mismatch repair deficiencies may accumulate tens of mutations per million nucleotides. By contrast, tumors of hematopoietic origin have less than one base substitution per million. Several bioinformatic tools and pipelines have been developed to efficiently detect somatic nucleotide substitutions through comparison of the genomic information obtained from paired normal and tumor samples from the same patient. Likewise, there are a number of publicly available computational methods to predict the functional relevance of the identified mutations in cancer specimens.60 Most of these bioinformatic tools exclusively deal with nucleotide changes in protein coding regions and evaluate the putative structural or functional effect of an amino acid substitution in a determined protein, thus obviating changes in other genomic regions, which can also be of crucial interest in cancer. In any case, current computational methods used in this regard are far from being optimal, and experimental validation is finally required to assess the functional relevance of nucleotide substitutions found in cancer genomes.
For years, the main focus of cancer genome analyses has been on identifying coding mutations that cause a change in the amino acid sequence of a gene. The rationale behind this is quite sound because any mutation that creates a novel protein or truncates an essential protein has the potential to drastically change the cellular environment. Examples of this have been shown earlier in the chapter with BRAF and KRAS along with many others. With the advancements in next-generation sequencing, larger studies are able to be conducted. These studies give the power to detect mutations occurring in the cancer genome at a lower frequency. Interesting to note is that these studies are leading to the discovery that recurrent synonymous mutations occur in cancer. Previously believed to be merely neutral mutations that maintain no functional role in tumorigenesis, these mutations were largely ignored, but a recent study shows105 that simply dismissing these mutations as silent may be premature.
In a review of only 29 melanoma exomes and genomes, 16 recurring synonymous mutations were discovered. When these mutations were screened in additional samples, a synonymous mutation in the gene BCL2L12 was discovered in 12 out of 285 total samples. The observed frequency of this recurrent mutation is greater than expected by chance, suggesting that it has undergone some type of selective pressure during tumor development.105 Noting that BCL2L12 had previously been linked to tumorigenesis, the mutation was further evaluated for its functional effect, with the finding that it led to an abrogation of the effect of a miRNA, leading to the deregulated expression of BCL2L12. BCL2L12 is a negative regulator of the gene p53, which functions by binding and inhibiting apoptosis in glioma.106 Accordingly, the dysregulation observed in BCL2L12 led to a reduction in p53 target gene expression.
Small insertions and deletions (indels) represent a second category of somatic mutations that can be discovered by whole-genome sequencing of cancer specimens. These mutations are about 10-fold less frequent than nucleotide substitutions, but may also have an obvious impact in cancer progression. Accordingly, specific bioinformatic tools have been created to detect these indels in the context of the large amount of information generated by whole-genome sequencing projects.107
The systematic identification of large chromosomal rearrangements in cancer genomes represents one of the most successful applications of next-generation sequencing methodologies. Previous strategies in this regard had mainly been based on the utilization of cytogenetic methods for the identification of recurrent translocations in hematopoietic tumors. More recently, a combination of bioinformatics and functional methods has allowed for the finding of recurrent translocations in solid epithelial tumors such as TMPRSS2–ERG in prostate cancer and EML4–ALK in non–small-cell lung cancer.108,109 Now, by using a next-generation sequencing analysis of genomes and transcriptomes, it is possible to systematically search for both intrachromosomal and interchromosomal rearrangements occurring in cancer specimens. These studies have already proven their usefulness for cancer research through the discovery of recurrent translocations involving genes of the RAF kinase pathway in prostate and gastric cancers and in melanomas.110 Likewise, massively parallel paired-end genome and transcriptome sequencing has already been used to detect new gene fusions in cancer and to catalog all major structural rearrangements present in some tumors and cancer cell lines.63,111–113 The ongoing cancer genome projects involving thousands of tumor samples will likely lead to the detection of many other chromosomal rearrangements of relevance in specific subsets of cancers. It is also remarkable that whole-genome sequencing may also facilitate the identification of other types of genomic alterations, including rearrangements of repetitive elements, such as active retrotransposons, or insertions of foreign gene sequences, such as viral genomes, which can contribute to cancer development. Indeed, a next-generation sequencing analysis of the transcriptome of Merkel cell carcinoma samples has revealed the clonal integration within the tumor genome of a previously unknown polyomavirus likely implicated in the pathogenesis of this rare but aggressive skin cancer.114
Finally, next-generation sequencing approaches have also demonstrated their feasibility to analyze the pattern of copy number alterations in cancer, because they allow researchers to count the number of reads in both tumor and normal samples at any given genomic region and then to evaluate the tumor-to-normal copy number ratio at this particular region. These new methods offer some advantages when compared with those based on microarrays, including much better resolution, precise definition of the involved breakpoints, and absence of saturation, which facilitates the accurate estimation of high copy number levels occurring in some genomic loci of malignant tumors.60
PATHWAY-ORIENTED MODELS OF CANCER GENOME ANALYSIS
Genomewide mutational analyses suggest that the mutational landscape of cancer is made up of a handful of genes that are mutated in a high fraction of tumors, otherwise known as mountains, and most mutated genes are altered at relatively low frequencies, otherwise known as hills (Fig. 1.7).29 The mountains probably give a high selective advantage to the mutated cell, and the hills might provide a lower advantage, making it hard to distinguish them from passenger mutations. Because the hills differ between cancer types, it seems that the cancer genome is more complex and heterogeneous than anticipated. Although highly heterogeneous, bioinformatic studies suggest that the mountains and hills can be grouped into sets of pathways and biologic processes. Some of these pathways are affected by mutations in a few pathway members and others by numerous members. For example, pathway analyses have allowed for the stratification of mutated genes in pancreatic adenocarcinomas to 12 core pathways that have at least one member mutated in 67% to 100% of the tumors analyzed (Fig. 1.8).45 These core pathways deviated to some that harbored one single highly mutated gene, such as in KRAS in the G1/S cell cycle transition pathway and pathways where a few mutated genes were found, such as the transforming growth factor (TGF-β) signaling pathway. Finally, there were pathways in which many different genes were mutated, such as invasion regulation molecules, cell adhesion molecules, and integrin signaling. Importantly, independent of how many genes in the same pathway are affected, if they are found to occur in a mutually exclusive fashion in a single tumor, they most likely give the same selective pressure for clonal expansion.
The idea of genetically analyzing pathways rather than individual genes has been applied previously, revealing the concept of mutual exclusivity. Mutual exclusivity has been shown elegantly in the case of KRAS and BRAF, where a KRAS-mutated cancer generally does not also harbor a BRAF mutation, because KRAS is upstream of BRAF in the same pathway.9 A similar concept was applied for PIK3CA and PTEN, where both mutations do not usually occur in the same tumor.23
With the ever expanding amounts of genetic information being gathered, the ability to search for common pathways being affected in cancer is increasing. One new pathway that is beginning to emerge is the glutamate-signaling pathway. Glutamate dysregulation has been implicated in a number of cancers. In a study of pancreatic duct adenocarcinoma (PDAC), it was seen that glutamate levels were significantly higher in the tissue of individuals with chronic pancreatitis (CP) and PDAC when compared to normal pancreas tissue.115 It was also observed that the increased glutamate levels led to proinvasion and antiapoptotic signaling through the activation of AMPA receptors.
Also in this regard, and through the use of whole-exome sequencing, it has been recently shown that the glutamate receptor gene GRIN2A is highly mutated in melanoma. The finding that many of these mutations are nonsense has suggested that GRIN2A is a novel tumor suppressor. Additional genes in the glutamate pathway have also found mutated in melanomas.116 Pathway analyses and statistical testing on the whole-exome data have also revealed the glutamate signaling pathway to be dysregulated. These results have been further corroborated in another study reporting mutations in the metabotropic glutamate receptor GRM3117,118 in melanoma. A functional analysis of mutations found in GRM3 in melanoma tumor samples has shown an increased activation of MEK1/2 kinase, increased migration, and anchorage-independent growth.117
Passenger and Driver Mutations
By the time a cancer is diagnosed, it is comprised of billions of cells carrying DNA abnormalities, some of which have a functional role in malignant proliferation; however, many genetic lesions acquired along the way have no functional role in tumorigenesis.14 The emerging landscapes of cancer genomes include thousands of genes that were not previously linked to tumorigenesis but are found to be somatically mutated. Many of these changes are likely to be passengers, or neutral, in that they have no functional effects on the growth of the tumor.14 Only a small fraction of the genetic alterations are expected to drive cancer evolution by giving cells a selective advantage over their neighbors. Passenger mutations occur incidentally in a cell that later or in parallel develops a driver mutation, but are not ultimately pathogenic.119 Although neutral, cataloging passengers mutations is important because they incorporate the signatures of the previous exposures the cancer cell underwent as well as DNA repair defects the cancer cell has. In many cases, the passenger and driver mutations occur at similar frequencies and the identification of drivers versus the passenger is of utmost relevance and remains a pressing challenge in cancer genetics.120–122 This goal will eventually be achieved through a combination of genetic and functional approaches, some of which are listed as follows.
The most reliable indicator that a gene was selected for and therefore is highly likely to be pathogenic is the identification of recurrent mutations, whether at the same exact amino acid position or in neighboring amino acid positions in different patients. More than that, if somatic alterations in the same gene occur very frequently (mountains in the tumor genome landscape), these can be confidently classified as drivers. For example, cancer alleles that are identified in multiple patients and different tumors types, such as those found in KRAS, TP53, PTEN, and PIK3CA, are clearly selected for during tumorigenesis.
However, most genes discovered thus far are mutated in a relatively small fraction of tumors (hills), and it has been clearly shown that genes that are mutated in less than 1% of patients can still act as drivers.123 The systematic sequencing of newly identified putative cancer genes in the vast number of specimens from cancer patients will help in this regard. However, even if examining large numbers of samples can provide helpful information to classify drivers versus passengers, this approach alone is limited by the marked variation in mutation frequency among individual tumors and individual genes. The statistical test utilized in this case calculates the probability that the number of mutations in a given gene reflects a mutation frequency that is greater than expected from the nonfunctional background mutation rate,29,124 which is different between different cancer types. These analyses incorporate the number of somatic alterations observed, the number of tumors studied, and the number of nucleotides that were successfully sequenced and analyzed.
Another approach often used to distinguish driver from passenger mutations exploits the statistical analysis of synonymous versus nonsynonymous changes.125 In contrast to nonsynonymous mutations, synonymous mutations do not alter the protein sequence. Therefore, they do not usually apply a growth advantage and would not be expected to be selected during tumorigenesis. This strategy works by comparing the observed-to-expected ratio of synonymous with that of nonsynonymous mutation. An increased proportion of nonsynonymous mutations from the expected 2:1 ratio implies selection pressure during tumorigenesis.
Other approaches are based on the concept that driver mutations may have characteristics similar to those causing Mendelian disease when inherited in the germ line and may be identifiable by constraints on tolerated amino acid residues at the mutated positions. In contrast, passenger mutations may have characteristics more similar to those of nonsynonymous SNPs with high minor allele frequencies. Based on these premises, supervised machine learning methods have been used to predict which missense mutations are drivers.126 Additional approaches to decipher drivers from passengers include the identification of mutations that affect locations that have previously been shown to be cancer causing in protein members of the same gene family. Enrichment for mutations in evolutionarily conserved residues are analyzed by algorithms, such as SIFT (sorting intolerant from tolerant (SIFT),127 which estimates the effects of the different mutations identified.
Probably the most conclusive methods to identify driver mutations will be rigorous functional studies using biochemical assays as well as model organisms or cultured cells, using knockout and knockin of individual cancer alleles.128 Unfortunately, these methods are not well suited to the analysis of the hundreds of gene candidates that arise from every large-scale cancer genome project. In conclusion, it is fair to say that sequencing cancer genomes is only the beginning of a journey that will ultimately be completed when the thousands of the newly discovered alleles are annotated as being the drivers of this disease. A summary of the various next-generation applications and approaches for their analysis is summarized in Figure 1.9 and Table 1.2.
NETWORKS OF CANCER GENOME PROJECTS
The repertoire of oncogenic mutations is extremely heterogeneous, suggesting that it would be difficult for independent cancer genome initiatives to address the generation of comprehensive catalogs of mutations in the wide spectrum of human malignancies. Accordingly, there have been different efforts to coordinate the cancer genome sequencing projects being carried out around the world, including The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC). Moreover, there are other initiatives that are more focused on specific tumors, such as that led by scientists at St. Jude Children’s Research Hospital in Memphis, and Washington University, which aims at sequencing multiple pediatric cancer genomes.129
TCGA began in 2006 in the United States as a comprehensive program in cancer genomics supported by the U.S. National Institutes of Health (NIH). The initial project focused on three tumors: GBM, serous cystadenocarcinoma of the ovary, and lung squamous carcinoma. These studies have already generated novel and interesting information regarding genes mutated in these malignancies.134 On the basis of these positive results, the NIH announced an expansion of the TCGA program with the aim to produce genomic data sets for at least 20 to 25 cancers during the next few years.
The ICGC was formed in 2008 to coordinate the generation of comprehensive catalogs of genomic abnormalities in tumors from 50 different cancer types or subtypes that are of clinical and societal importance across the world.130The project aims to perform systematic studies of over 25,000 cancer genomes at the genomic level and integrate this information with epigenomic and transcriptomic studies of the same cases as well as with clinical features of patients. At present, there are a total of 69 committed projects involving at least 16 different countries coordinated by the ICGC. All of these projects deal with at least 500 samples per cancer type from cancers affecting a variety of human organs and tissues, including blood, the brain, the breast, the esophagus, the kidneys, the liver, the oral cavity, the ovaries, the pancreas, the prostate, the skin, and the stomach.130
All of these coordinated projects have already provided new insights into the catalog of genes mutated in cancer and have unveiled specific signatures of the mutagenic mechanisms, including carcinogen exposures or DNA-repair defects, implicated in the development of different malignant tumors.83,84,87,131 Furthermore, these cancer genome studies have also contributed to define clinically relevant subtypes of tumors for prognosis and therapeutic management, and in some cases have identified new targets and strategies for cancer treatment.74–76 The rapid technological advances in DNA sequencing will likely drop the costs of sequencing cancer genomes to a small fraction of the current price and will allow researchers to overcome some of the current limitations of these global sequencing efforts. Hopefully, worldwide coordination of cancer genome projects, including Pan-Cancer initiative, with those involving large-scale, functional analyses of genes in both cellular and animal models will likely provide us with the most comprehensive collection of information generated to date about the causes and molecular mechanisms of cancer.
THE GENOMIC LANDSCAPE OF CANCERS
Examining the overall distribution of the identified mutations redefined the cancer genome landscapes whereby the mountains are the handful of commonly mutated genes and the hills represent the vast majority of genes that are infrequently mutated. One of the most striking features of the tumor genomic landscape is that it involves different sets of cancer genes that are mutated in a tissue-specific fashion.132,133 To continue with the analogy, the scenery is very different if we observe a colorectal, a lung, or a breast tumor. This indicates that mutations in specific genes cause tumors at specific sites, or are associated with specific stages of development, cell differentiation, or tumorigenesis, despite many of those genes being expressed in various fetal and adult tissues. Moreover, different types of tumors follow specific genetic pathways in terms of the combination of genetic alterations that it must acquire. For example, no cancer outside the bowel has been shown to follow the classic genetic pathway of colorectal tumorigenesis. Additionally, KRAS mutations are almost always present in pancreatic cancers but are very rare or absent in breast cancers. Similarly, BRAF mutations are present in 60% of melanomas, but are very infrequent in lung cancers.1 Another intriguing feature is that alterations in ubiquitous housekeeping genes, such as those involved in DNA repair or energy production, occur only in particular types of tumors.
In addition to tissue specificity, the genomic landscape of tumors can also be associated with gender and hormonal status. For example, HER2 amplification and PIK3C2A mutations, two genetic alterations associated with breast cancer development, are correlated with the estrogen-receptor hormonal status.134 The molecular basis for the occurrence of cancer mutations in tissue- and gender-specific profiles is still largely unknown. Organ-specific expression profiles and cell-specific neoplastic transformation requirements are often mentioned as possible causes for this phenomenon. Identifying tissue and gender cancer mutations patterns is relevant because it may allow for the definition of individualized therapeutic avenues.
INTEGRATIVE ANALYSIS OF CANCER GENOMICS
The implementation of novel high-throughput technologies is generating an extraordinary amount of information on cancer samples in many different ways other than those derived from whole-exome or whole-genome sequencing. Accordingly, there is a growing need to integrate genomic, epigenomic, transcriptomic, and proteomic landscapes from tumor samples, and then linking this integrated information with clinical outcomes of cancer patients. There are some examples of human malignancies in which this integrative approach has been already performed, such as for AML, glioblastoma, medulloblastoma, and renal cell, colorectal, ovarian, endometrial, prostate, and breast carcinomas.135–142 In these cases, the integration of whole-exome and whole-genome sequencing with studies involving genomic DNA copy number arrays, DNA methylation, transcriptomic arrays, miRNA sequencing, and proteomic profiling has contributed to improving the molecular classification of complex and heterogeneous tumors. These integrative molecular analyses have also provided new insights into the mechanisms disrupted in each particular cancer type or subtype and have facilitated the association of genomic information with distinct clinical parameters of cancer patients and the discovery of novel therapeutic targets.143 Also in this regard, there has been significant progress in the definition of the mechanisms by which the cancer genome and epigenome influence each other and cooperate to facilitate malignant transformation.144,145 Thus, many tumor-suppressor genes are inactivated by either mutation or epigenetic silencing, and in some cases such as colorectal carcinomas, both mechanisms work coordinately to create a permissive environment for oncogenic transformation.146 Moreover, mutations in epigenetic regulators such as DNA methyl transferases, chromatin remodelers, histones, and histone modifiers, are very frequent events in many tumors, including hepatocellular carcinomas, renal carcinomas leukemias, lymphomas, glioblastomas, and medulloblastomas. These genetic alterations of epigenetic modulators cause widespread transcriptomic changes, thereby amplifying the initial effect of the mutational event at the cancer genome level.145
The recent availability of different platforms for integrative cancer genome analyses will be very helpful in enabling the classification, biologic characterization, and personalized clinical management of human cancers (Table 1.3).144,147
THE CANCER GENOME AND THE NEW TAXONOMY OF TUMORS
Deciphering the cancer genome has already impacted clinical practice at multiple levels. On the one hand, it allowed for the identification of new cancer genes such as IDH1, a gene involved in glioma, which was discovered recently (see previous), and on the other hand, it is redesigning the taxonomy of tumors.
Until the genomic revolution, tumors had been classified based on two criteria: their localization (site of occurrence) and their appearance (histology). These criteria are also currently used as primary determinants of prognosis and to establish the best treatments. For many decades, it has been known that patients with histologically similar tumors have different clinical outcomes. Furthermore, tumors that cannot be distinguished based on an histologic analysis can respond very differently to identical therapies.148
It is becoming increasingly clear that the frequency and distribution of mutations affecting cancer genes can be used to redefine the histology-based taxonomy of a given tumor type. Lung and colorectal tumors represent paradigmatic examples. Genomic analyses led to the identification of activating mutations in the receptor tyrosine kinase EGFR in lung adenocarcinomas.149 The occurrence of EGFRmutations molecularly defines a subtype of non–small-cell lung cancers (NSCLC) that occur mainly in nonsmoking women, that tend to have a distinctly enhanced prognosis, and that typically respond to epidermal growth factor receptor (EGFR)-targeted therapies.150–152 Similarly, the recent discovery of the EML4-ALK fusion identifies yet another subset of NSCLC that is clearly distinct from those that harbor EGFR mutations, that have distinct epidemiologic and biologic features, and that respond to ALK inhibitors.109,153
The second example is colorectal cancers (CRC), the tumor type for which the genomic landscape has been refined with the highest accuracy. CRCs can be clearly categorized according to the mutational profile of the genes involved in the KRAS pathway (Fig. 1.10). It is now known that KRAS mutations occur in approximately 40% of CRCs. Another subtype of CRC (approximately 10%) harbors mutations in BRAF, the immediate downstream effectors of KRAS.10
In CRCs and other tumor types, KRAS and BRAF mutations are known to be mutually exclusive. The mutual exclusivity pattern indicates that these genes operate in the same signaling pathway. Large epidemiologic studies have shown that the prognosis of tumors harboring wild-type KRAS/BRAF genes is distinct, and typically more favorable, than that of the mutated ones.154,155 Of note, KRAS and BRAFmutations have been recently shown to impair responsiveness to the anti-EGFR monoclonal antibodies therapies in CRC patients.156–158 Clearly distinct subgroups can be genetically identified in both NSCLCs and CRCs with respect to prognosis and response to therapy. It is likely that as soon as the genomic landscapes of other tumor types are defined, molecular subgroups like those described previously will also become defined.
Genotyping tumor tissue in search of somatic genetic alterations for actionable information has become routine practice in clinical oncology. The genetic profile of solid tumors is currently obtained from surgical or biopsy specimens. As the techniques that have enabled us to analyze tumor tissues become ever more sophisticated, we have realized the limitations of this approach. As previously discussed, cancers are heterogeneous, with different areas of the same tumor showing different genetic profiles (i.e., intratumoral heterogeneity); likewise, heterogeneity exists between metastases within the same patient (i.e., intermetastatic heterogeneity).159 A tissue section (or a biopsy) from one part of a solitary tumor will miss the molecular intratumoral as well as intermetastatic heterogeneity. To capture tumor heterogeneity, techniques that are capable of interrogating the genetic landscapes of the overall disease in a single patient are needed.
In 1948, the publication of a manuscript describing the presence of cell-free circulating DNA (cfDNA) in the blood of humans offered—probably without realizing it—unprecedented opportunities in this area.160 Only recently, the full potential of this seminal discovery has been appreciated. Several groups have reported that the analysis of circulating tumor DNA can, in principle, provide the same genetic information obtained from tumor tissue.161 The levels of cfDNA are typically higher in cancer patients than healthy individuals, indicating that it is possible to screen for the presence of disease through a simple blood test. Furthermore, the specific detection of tumor-derived cfDNA has been shown to correlate with tumor burden, which changes in response to treatment or surgery.162–164
Although the detection of ctDNA has remarkable potential, it is also challenging for several reasons. The first is the need to discriminate DNA released from tumor cells (ctDNA) from circulating normalDNA. Discerning ctDNA from normal cfDNA is aided by the fact that tumor DNA is defined by the presence of mutations. These somatic mutations, commonly single base pair substitutions, are present only in the genomes of cancer cells or precancerous cells and are present in the DNA of normal cells of the same individual. Accordingly, ctDNA offers exquisite specificity as a biomarker. Unfortunately, cfDNA derived from tumor cells often represents a very small fraction (<1%) of the total cfDNA, thus limiting the applicability of the approach. The development and refinement of next-generation sequencing strategies as well as recently developed digital PCR techniques have made it possible to define rare mutant variants in complex mixtures of DNA. Using these approaches, it is possible to detect point mutations, rearrangements, and gene copy number changes in individual genes starting from a few milliliters of plasma.165 Very recently, several groups have opened a new frontier by showing that exome analyses can also be performed from circulating DNA extracted from the blood of cancer patients.166
The detection of tumor-specific genetic alterations in patients’ blood (often referred to as liquid biopsies) has several applications in the field of oncology, which are summarized as follows. Analyses of cfDNA can be used to genotype tumors when a tissue sample is not available or is difficult to obtain. Circulating tumor DNA fragments contain the identical genetic defects as the tumor themselves, thus the blood can reveal tumor point mutations (EGFR, KRAS, BRAF, PIK3CA), rearrangements (e.g., EML4-ALK), as well as tumor amplifications (MET).167–169 Liquid biopsies may also be useful in monitoring tumor burden—a central aspect in the management of patients with cancer that is typically assessed with imaging. In this regard, several investigational studies have shown that ctDNA can be a surrogate for tumor burden and that, much like viral load changes (e.g., HIV viral load), levels of ctDNA correspond with clinical course. Another application of ctDNA is the detection of minimal residual disease following surgery or therapy with curative intent.163 Finally, liquid biopsies can be used to monitor the genomic drift (clonal evolution) of tumors upon treatment.166 In this setting, the analysis of ctDNA in plasma samples obtained pretreatment, during, and posttreatment can lead to an understanding of the mechanisms of primary and, especially, acquired resistance to therapies.170,171
Importantly, the advances in sequencing technologies have made the idea of personalized treatment of cancer a reality, which is most evident in the field of adoptive cell therapy (ACT). Although already a treatment in use, the ability to use a patient’s autologous tumor-infiltrating lymphocytes (TIL) is in position to benefit greatly from advances in sequencing technologies. A recent study demonstrated this when whole-exome data, along with a major histocompatibility complex (MHC)-binding algorithm, were utilized to identify candidate tumor epitopes that are recognized by the patients’ TILs.172 This study should allow for future work in which the information obtained from the direct sequencing of a patient’s tumor can quickly be used to generate tumor-reactive T cells that can then be used for a personalized treatment.
In conclusion, the taxonomy of tumors is being rewritten using the presence of genetic lesions as major criteria. Genome-based information will improve the diagnosis and will be used to determine personalized therapeutic regimens based on the genetic landscape of individual tumors.
CANCER GENOMICS AND DRUG RESISTANCE
Cancer genomics has dramatically impacted disease management, because its application is helping researchers determine which patients are likely to benefit from which drug. As discussed in great detail in Chapter 22, good examples for such treatment include targeted therapy using imatinib for chronic myeloid leukemia (CML) patients and the use of gefitinib and erlotinib for NSCLC patients.
The key to the successful development and application of anticancer agents is a better understanding of the effect of the therapeutic regimens and of resistance mechanisms that may develop. In most tumor types, a fraction of patients’ tumors are refractory to therapies (intrinsic resistance). Even if an initial response to therapies is obtained, the vast majority of tumors subsequently become refractory (i.e., acquired resistance), and patients eventually succumb to disease progression. Therefore, secondary resistance should be regarded as a key obstacle to treatment progress. The analysis of the cancer genome represents a powerful tool both for the identification of chemotherapeutic signatures as well as to understand resistance mechanisms to therapeutic agents. Examples for each of these are described as follows.
An important application of systematic sequencing experiments is the identification of the effects of chemotherapy on the cancer genome. For example, gliomas that recur after temozolomide treatment have been shown to harbor large numbers of mutations with a signature typical of a DNA alkylating agent.173,174 Because these alterations were detected using Sanger sequencing, which as described previously has limited sensitivity, the data suggested that the detected alterations were clonal. The model that unfolds from this study indicates that although temozolomide has limited efficacy, almost all of the cells in a glioma respond to the drug. However, a single cell that was resistant to the chemotherapy proliferated and formed a cell clone. Later genomic analyses of the cell clone allowed for the identification of the underlying mutated resistance genes.173,174
Single-molecule–targeted therapy is almost always followed by acquired drug resistance.175–177 Genomic analyses can be successfully exploited to decipher resistance mechanisms to such inhibitors. A few paradigmatic examples are presented as follows, which will be discussed extensively in other chapters. Despite the effectiveness of gefitinib and erlotinib in EGFR mutant cases of NSCLC,178 drug resistance develops within 6 to 12 months after the initiation of therapy. The underlying reason for this resistance was identified as a secondary mutation in EGFR exon 20, T790M, which is detectable in 50% of patients who relapse.179–181 Importantly, some studies have shown the mutation to be present before the patient was treated with the drug,182,183 suggesting that exposure to the drug selected for these cells.184 Because the drug-resistant EGFR mutation is structurally analogous to the mutated gatekeeper residue T315I in BCR-ABL, T670I in c-Kit, and L1196M in EML4-ALK, which have been shown previously to confer resistance to imatinib and other kinase inhibitors,176,185,186 this mechanism of resistance represents a general problem that needs to be overcome.
A recent elegant study, which also represents the use of genomics in understanding drug-resistance mechanisms, focused on the inhibition of activating BRAF (V600E) mutations, which occur in 7% of human malignancies and in 60% of melanomas.9 Clinical trials using PLX4032, a novel class I RAF-selective inhibitor, showed an 80% antitumor response rate in melanoma patients with BRAF (V600E) mutations; however, cases of drug resistance were observed.187 The use of microarray and sequencing technologies showed that, in this case, the resistance was not due to secondary mutations in BRAF, but due rather to either upregulation of PDGFRB or NRAS mutations.188
It was, however, the introduction of two anti-EGFR monoclonal antibodies, cetuximab and panitumumab, for the treatment of metastatic colorectal cancer, that provided the largest body of knowledge on the relationship between tumors’ genotypes and the response to targeted therapies. The initial clinical analysis pointed out that only a fraction of metastatic CRC patients benefited from this novel treatment. Different from the NSCLC paradigm, it was found that EGFR mutations do not play a major role in the response. On the contrary, from the initial retrospective analysis, it became clear that somatic KRASmutations, thought to be present in 35% to 45% of metastatic colorectal cancers, are important negative predictors of efficacy in patients who are given panitumumab or cetuximab.156–158 Among tumors carrying wild-type KRAS, mutations of BRAF or PIK3CA, or a loss of phosphatase and tensin homolog (PTEN) expression may also predict resistance to EGFR-targeted monoclonal antibodies, although the latter biomarkers require further validation before they can be incorporated into clinical practice. From these few examples, it is clear that a future, deeper genomic understanding of targeted drug resistance is crucial to the effective development of additional as well as alternative therapies to overcome this resistance.
PERSPECTIVES OF CANCER GENOME ANALYSIS
The completion of the human genome project has marked a new beginning in biomedical sciences. Because human cancer is a genetic disease, the field of oncology has been one of the first to be impacted by this historic revolution. Knowledge of the sequence and organization of the human genome allows for the systematic analysis of the genetic alterations underlying the origin and evolution of tumors. High-throughput mutational profiling of common tumors, including lung, skin, breast, and colorectal cancers, and the application of next-generation sequencing to whole genome, whole exome, and whole transcriptome of cancer samples has allowed substantial advances in the understanding of this disease by facilitating the detection of all main types of somatic cancer genome alterations. These have also led to historical results, such as the identification of genetic alterations that are likely to be the major drivers of these diseases.
However, the genetic landscape of cancers is by no means complete, and what has been learned so far has raised new and exciting questions that must be addressed. There are still important technical challenges for the detection of somatic mutations.60 Clinical tumor samples often contain large amounts of nonmalignant cells, which makes the identification of mutations in cancer genomes more challenging when compared with similar analyses of peripheral blood samples for germ-line genome studies. Moreover, the genomic instability inherent to cancer development and progression largely increases the complexity and diversity of genomic alterations of malignant tumors, making it necessary to distinguish between driver and passenger mutations. Likewise, the fact that malignant tumors are genetically heterogeneous and contain several clones simultaneously growing within the same tumor mass raises additional questions regarding the quality of the information currently derived from cancer genomes. Hopefully, in the near future, advances in third-generation sequencing technologies will make it feasible to obtain high-quality sequence data of a genome isolated from a single cell, an aspect of crucial relevance for cancer research.
One of the next imperatives is the definition of the oncogenomic profile of all tumor types. In particular, the less common—although not less lethal—ones are still largely mysterious to scientists and untreatable to clinicians. For some of these diseases, few new therapeutically amenable molecular targets have been discovered in the past years. For example, the identification of drugable genetic lesions associated with pancreatic and ovarian cancers could help define new therapeutic strategies for these aggressive diseases. To achieve this, detailed oncogenomic maps of the corresponding tumors must be drafted. The latter will hopefully be completed in the coming years, thanks to the systematic cancer genome projects that are presently being performed.
Even in the case of common cancers, a lot of genomic profiling efforts still lay ahead. For example, in a significant fraction of breast and lung tumors, the mutations that are likely to be drivers have not yet been found. This is not surprising considering that even in these tumor types only a limited number of samples have been systematically analyzed so far. Therefore, low incidence mutations that could represent potentially key therapeutic targets in a subset of tumors might have escaped detection. Consequently, the scaling up of the mutational profiling to large numbers of specimens for each tumor type is warranted.
Finally, understanding the cellular properties imparted by the hundreds of recently discovered cancer alleles is another area that must be developed. As a matter of fact, compared to the genomic discovery stage, the functional validation of putative novel cancer alleles, despite their potential clinical relevance, is substantially lagging behind. To achieve this, high-throughput functional studies in model systems that accurately recapitulate the genetic alterations found in human cancer must be developed.
To conclude, the eventual goal of profiling the cancer genome is not only to further understand the molecular basis of the disease, but also to discover novel diagnostic and drug targets. One might anticipate that the most immediate application of these new technologies will be noninvasive strategies for early cancer detection. Considering that oncogenic mutations are present only in cancer cells, screening for tumor-derived mutant DNA in patients’ blood holds great potential and will progressively substitute current biomarkers, which have poor sensitivity and lack specificity.171 Further improvements in next-generation sequencing technologies are likely to reduce their cost as well as make these analyses more facile in the future. Once this happens, most cancer patients will undergo in-depth genomic analyses as part of their initial evaluation and throughout their treatment. This will offer more precise diagnostic and prognostic information, which will affect treatment decisions. Although many challenges remain, the information gained from next-generation sequencing platforms is laying a foundation for personalized medicine, in which patients are managed with therapies that are tailored to the specific gene mutations found in their tumors. Ultimately, these should lead to therapeutic successes similar to the ones attained for CML patients with imatinib,189,190melanoma patients with PLX4032,187 and NSCLC patients with gefitinib and erlotinib.178 Clearly, this is the absolute goal for all of this work.
ACKNOWLEDGMENTS
This work was supported by the Intramural Research Programs of the National Human Genome Research Institute, National Institutes of Health, USA, YS is supported by the Henry Chanoch Krenter Institute for Biomedical Imaging and Genomics, Louis and Fannie Tolz Collaborative Research Project, Dukler Fund for Cancer Research, De Benedetti Foundation-Cherasco 1547, Peter and Patricia Gruber Awards, Gideon Hamburger, Israel, Estate of Alice Schwarz-Gardos, Estate of John Hunter and the Knell Family. YS is supported by the Israel Science Foundation grant numbers 1604/13 and 877/13 and the ERC (StG-335377). A.B. is supported by the European Communityís Seventh Framework Programme under grant agreement no. 259015 COLTHERES, Associazione Italiana per la Ricerca sul Cancro (AIRC) IG grant no. 12812 and Fondazione Piemontese per la Ricerca sul Cancro–ONLUS. C.L-O. is an Investigator of the Botin Foundation supported by grants from Ministerio de Economía y Competitividad-Spain and Instituto de Salud Carlos III (RTICC), Spain.
REFERENCES
1. Vogelstein B, Kinzler KW. Cancer genes and the pathways they control. Nat Med 2004;10:789–799.
2. Kinzler KW, Vogelstein B. Lessons from hereditary colon cancer. Cell 1996;87:159–170.
3. International Human Genome Sequencing Consortium. Finishing the euchromatic sequence of the human genome. Nature 2004;431:931–945.
4. Stehelin D, Varmus HE, Bishop JM, et al. DNA related to the transforming gene(s) of avian sarcoma viruses is present in normal avian DNA. Nature 1976;260:170–173.
5. Rous P. Transmission of a malignant new growth by means of a cell-free filtrate. J Am Med Assoc 1911;56:198.
6. International HapMap Consortium. The International HapMap Project. Nature 2003;426:89–96.
7. International HapMap Consortium. A haplotype map of the human genome. Nature 2005;437:1299–1320.
8. Sanger F, Nicklen S, Coulson AR. DNA sequencing with chain-terminating inhibitors. Proc Natl Acad Sci U S A 1977;74:5463–5467.
9. Davies H, Bignell GR, Cox C, et al. Mutations of the BRAF gene in human cancer. Nature 2002;417:949–954.
10. Rajagopalan H, Bardelli A, Lengauer C, et al. Tumorigenesis: RAF/RAS oncogenes and mismatch-repair status. Nature 2002;418:934.
11. Moodie SA, Wolfman A. The 3Rs of life: Ras, Raf and growth regulation. Trends Genet 1994;10:44–48.
12. Hafen E, Dickson B, Brunner D, et al. Genetic dissection of signal transduction mediated by the sevenless receptor tyrosine kinase in Drosophila. Prog Neurobiol 1994;42:287–292.
13. Bardelli A, Parsons DW, Silliman N, et al. Mutational analysis of the tyrosine kinome in colorectal cancers. Science 2003;300:949.
14. Greenman C, Stephens P, Smith R, et al. Patterns of somatic mutation in human cancer genomes. Nature 2007;446:153–158.
15. Samuels Y, Wang Z, Bardelli A, et al. High frequency of mutations of the PIK3CA gene in human cancers. Science 2004;304:554.
16. Wang Z, Shen D, Parsons DW, et al. Mutational analysis of the tyrosine phosphatome in colorectal cancers. Science 2004;304:1164–1166.
17. Vivanco I, Sawyers CL. The phosphatidylinositol 3-Kinase AKT pathway in human cancer. Nat Rev Cancer 2002;2:489–501.
18. Broderick DK, Di C, Parrett TJ, et al. Mutations of PIK3CA in anaplastic oligodendrogliomas, high-grade astrocytomas, and medulloblastomas. Cancer Res 2004;64:5048–5050.
19. Lee JW, Soung YH, Kim SY, et al. PIK3CA gene is frequently mutated in breast carcinomas and hepatocellular carcinomas. Oncogene 2005;24:1477–1480.
20. Bachman KE, Argani P, Samuels Y, et al. The PIK3CA gene is mutated with high frequency in human breast cancers. Cancer Biol Ther 2004;3:772–775.
21. Oda K, Stokoe D, Taketani Y, et al. High frequency of coexistent mutations of PIK3CA and PTEN genes in endometrial carcinoma. Cancer Res 2005;65:10669–10673.
22. Samuels Y, Waldman T. Oncogenic mutations of PIK3CA in human cancers. Curr Top Microbiol Immunol 2010;2:21–42.
23. Parsons DW, Wang TL, Samuels Y, et al. Colorectal cancer: mutations in a signalling pathway. Nature 2005;436:792.
24. Lopez-Otin C, Overall CM. Protease degradomics: a new challenge for proteomics. Nat Rev Mol Cell Biol 2002;3:509–519.
25. Liotta LA, Tryggvason K, Garbisa S, et al. Metastatic potential correlates with enzymatic degradation of basement membrane collagen. Nature 1980;284:67–68.
26. Lopez-Otin C, Hunter T. The regulatory crosstalk between kinases and proteases in cancer. Nat Rev Cancer 2010;10:278–292.
27. Egeblad M, Werb Z. New functions for the matrix metalloproteinases in cancer progression. Nat Rev Cancer 2002;2:161–174.
28. Lopez-Otin C, Matrisian LM. Emerging roles of proteases in tumour suppression. Nat Rev Cancer 2007;7:800–808.
29. Wood LD, Parsons DW, Jones S, et al. The genomic landscapes of human breast and colorectal cancers. Science 2007;318:1108–1113.
30. Palavalli LH, Prickett TD, Wunderluch JR, et al. Analysis of the matrix metalloproteinase family reveals that MMP8 is often mutated in melanoma. Nat Genet 2009;41:518–520.
31. Lopez-Otin C, Palavalli LH, Samuels Y. Protective roles of matrix metalloproteinases: from mouse models to human cancer. Cell Cycle 2009;8:3657–3662.
32. Hanahan D, Weinberg RA. The hallmarks of cancer. Cell 2000;100:57–70.
33. Teitz T, Wei T, Valentine MB, et al. Caspase 8 is deleted or silenced preferentially in childhood neuroblastomas with amplification of MYCN. Nat Med 2000;6:529–535.
34. Mandruzzato S, Brasseur F, Andry G, et al. A CASP-8 mutation recognized by cytolytic T lymphocytes on a human head and neck carcinoma. J Exp Med 1997;186:785–793.
35. Soung YH, Lee JW, Kim SY, et al. CASPASE-8 gene is inactivated by somatic mutations in gastric carcinomas. Cancer Res 2005;65:815–821.
36. Fraile JM, Quesada V, Rodríguez D, et al. Deubiquitinases in cancer: new functions and therapeutic options. Oncogene 2012;31:2373–2388.
37. Bignell GR, Warren W, Seal S, et al. Identification of the familial cylindromatosis tumour-suppressor gene. Nat Genet 2000;25:160–165.
38. Schmitz R, Hansmann ML, Bohle V, et al. TNFAIP3 (A20) is a tumor suppressor gene in Hodgkin lymphoma and primary mediastinal B cell lymphoma. J Exp Med 2009;206:981–989.
39. Compagno M, Lim WK, Grunn A, et al. Mutations of multiple genes cause deregulation of NF-kappaB in diffuse large B-cell lymphoma. Nature 2009;459:717–721.
40. Kato M, Sanada M, Kato I, et al. Frequent inactivation of A20 in B-cell lymphomas. Nature 2009;459:712–716.
41. Novak U, Rinaldi A, Kwee I, et al. The NF-kappa B negative regulator TNFAIP3 (A20) is inactivated by somatic mutations and genomic deletions in marginal zone lymphomas. Blood 2009;113: 4918–4921.
42. Harbour JW, Onken MD, Roberson ED, et al. Frequent mutation of BAP1 in metastasizing uveal melanomas. Science 2010;330:1410–1413.
43. Carbone M, Yang H, Pass HI, et al. BAP1 and cancer. Nat Rev Cancer 2013;13:153–159.
44. Sjöblom T, Jones S, Wood LD, et al. The consensus coding sequences of human breast and colorectal cancers. Science 2006;314:268–274.
45. Jones S, Zhang X, Parsons DW, et al. Core signaling pathways in human pancreatic cancers revealed by global genomic analyses. Science 2008;321:1801–1806.
46. Parsons DW, Jones S, Zhang X, et al. An integrated genomic analysis of human glioblastoma multiforme. Science 2008;321:1807–1812.
47. Jones S, Wang TL, Shih leM, et al. Frequent mutations of chromatin remodeling gene ARID1A in ovarian clear cell carcinoma. Science 2010;330:228–231.
48. Parsons DW, Li M, Zhang X, et al. The genetic landscape of the childhood cancer medulloblastoma. Science 2011;331:435–439.
49. Yan H, Parsons DW, Jin G, et al. IDH1 and IDH2 mutations in gliomas. N Engl J Med 2009;360:765–773.
50. Bleeker FE, Lamba S, Leenstra S, et al. IDH1 mutations at residue p.R132 (IDH1(R132)) occur frequently in high-grade gliomas but not in other solid tumors. Hum Mutat 2009;30:7–11.
51. Hartmann C, Meyer J, Balss J, et al. Type and frequency of IDH1 and IDH2 mutations are related to astrocytic and oligodendroglial differentiation and age: a study of 1,010 diffuse gliomas. Acta Neuropathol 2009;118:469–474.
52. Hayden JT, Frühwald MC, Hasselblatt M, et al. Frequent IDH1 mutations in supratentorial primitive neuroectodermal tumors (sPNET) of adults but not children. Cell Cycle 2009;8:1806–1807.
53. Ichimura K, Pearson DM, Kocialkowski S, et al. IDH1 mutations are present in the majority of common adult gliomas but rare in primary glioblastomas. Neuro Oncol 2009;11:341–347.
54. Kang MR, Kim MS, Oh JE, et al. Mutational analysis of IDH1 codon 132 in glioblastomas and other common cancers. Int J Cancer 2009;125:353–355.
55. Watanabe T, Nobusawa S, Kleihues P, et al. IDH1 mutations are early events in the development of astrocytomas and oligodendrogliomas. Am J Pathol 2009;174:1149–1153.
56. Mardis ER, Ding L, Dooling DJ, et al. Recurring mutations found by sequencing an acute myeloid leukemia genome. N Engl J Med 2009;361:1058–1066.
57. Green A, Beer P. Somatic mutations of IDH1 and IDH2 in the leukemic transformation of myeloproliferative neoplasms. N Engl J Med 2010;362:369–370.
58. Gross S, Cairns RA, Minden Md, et al. Cancer-associated metabolite 2-hydroxyglutarate accumulates in acute myelogenous leukemia with isocitrate dehydrogenase 1 and 2 mutations. J Exp Med 2010;207:339–344.
59. Mardis ER, Wilson RK. Cancer genome sequencing: a review. Hum Mol Genet 2009;18:R163–R168.
60. Meyerson M, Gabriel S, Getz G. Advances in understanding cancer genomes through second-generation sequencing. Nat Rev Genet 2010;11:685–696.
61. Metzker ML. Sequencing technologies - the next generation. Nat Rev Genet 2010;11:31–46.
62. Bell DW. Our changing view of the genomic landscape of cancer. J Pathol 2010;220:231–243.
63. Campbell PJ, Pleasance ED, Stephens PJ, et al. Subclonal phylogenetic structures in cancer revealed by ultra-deep sequencing. Proc Natl Acad Sci U S A 2008;105:13081–13086.
64. Kidd JM, Cooper GM, Donahue WF, et al. Mapping and sequencing of structural variation from eight human genomes. Nature 2008;453:56–64.
65. Drmanac R, Sparks AB, Callow MJ, et al. Human genome sequencing using unchained base reads on self-assembling DNA nanoarrays. Science 2010;327:78–81.
66. Clarke J, Wu HC, Jayasinghe L, et al. Continuous base identification for single-molecule nanopore DNA sequencing. Nat Nanotechnol 2009;4:265–270.
67. Schadt EE, Turner S, Kasarskis A. A window into third-generation sequencing. Hum Mol Genet 2010;19:R227–R240.
68. Dressman D, Yan H, Traverso G, et al. Transforming single DNA molecules into fluorescent magnetic particles for detection and enumeration of genetic variations. Proc Natl Acad Sci U S A 2003;100:8817–8822.
69. Fedurco M, Romieu A, Williams S, et al. BTA, a novel reagent for DNA attachment on glass and efficient generation of solid-phase amplified DNA colonies. Nucleic Acids Res 2006;34:e22.
70. Harris TD, Buzby PR, Babcock H, et al. Single-molecule DNA sequencing of a viral genome. Science 2008;320:106–109.
71. Morozova O, Hirst M, Marra MA. Applications of new sequencing technologies for transcriptome analysis. Annu Rev Genomics Hum Genet 2009;10:135–151.
72. Pop M, Salzberg SL. Bioinformatics challenges of new sequencing technology. Trends Genet 2008;24:142–149.
73. Ley TJ, Mardis ER, Ding L, et al. DNA sequencing of a cytogenetically normal acute myeloid leukaemia genome. Nature 2008;456:66–72.
74. Garraway LA, Lander ES. Lessons from the cancer genome. Cell 2013;15:17–37.
75. Vogelstein B, Papadopoulos N, Velculescu VE, et al. Cancer genome landscapes. Science 2013;339:1546–1558.
76. Watson IR, Takahashi K, Futreal PA, et al. Emerging patterns of somatic mutations in cancer. Nat Rev Genet 2013;14:703–718.
77. Campbell PJ, Yachida S, Mudie LJ, et al. The patterns and dynamics of genomic instability in metastatic pancreatic cancer. Nature 2010;467:1109–1113.
78. Lee W, Jiang Z, Liu J, et al. The mutation spectrum revealed by paired genome sequences from a lung cancer patient. Nature 2010;465:473–477.
79. Marcucci G, Maharry K, Wu YZ, et al. IDH1 and IDH2 gene mutations identify novel molecular subsets within de novo cytogenetically normal acute myeloid leukemia: a Cancer and Leukemia Group B study. J Clin Oncol 2010;28:2348–2355.
80. Paschka P, Schlenk RF, Gaidzik VI, et al. IDH1 and IDH2 mutations are frequent genetic alterations in acute myeloid leukemia and confer adverse prognosis in cytogenetically normal acute myeloid leukemia with NPM1 mutation without FLT3 internal tandem duplication. J Clin Oncol 2010;28:3636–3643.
81. Shah SP, Morin Rd, Khattra J, et al. Mutational evolution in a lobular breast tumour profiled at single nucleotide resolution. Nature 2009;461:809–813.
82. Yachida S, Jones S, Bozic I, et al. Distant metastasis occurs late during the genetic evolution of pancreatic cancer. Nature 2010;467:1114–1117.
83. Pleasance ED, Cheetham RK, Stephens PJ, et al. A comprehensive catalogue of somatic mutations from a human cancer genome. Nature 2010;463:191–196.
84. Pleasance ED, Stephens PJ, O’Meara S, et al. A small-cell lung cancer genome with complex signatures of tobacco exposure. Nature 2010;463:184–190.
85. Ding L, Ellis MJ, Li S, et al. Genome remodelling in a basal-like breast cancer metastasis and xenograft. Nature 2010;464:999–1005.
86. Ley TJ, Ding L, Walter MJ, et al. DNMT3A mutations in acute myeloid leukemia. N Engl J Med 2010;363:2424–2433.
87. Alexandrov LB, Nik-Zainal S, Wedge DC, et al. Signatures of mutational processes in human cancer. Nature 2013;500:415–421.
88. Ward PS, Patel J, Wise DR, et al. The common feature of leukemia-associated IDH1 and IDH2 mutations is a neomorphic enzyme activity converting alpha-ketoglutarate to 2-hydroxyglutarate. Cancer Cell 2010;17:225–234.
89. Jones SJ, Laskin J, Lu YY, et al. Evolution of an adenocarcinoma in response to selection by targeted kinase inhibitors. Genome Biol 2010;11:R82.
90. Navin N, Kendall J, Troge J, et al. Tumour evolution inferred by single-cell sequencing. Nature 2011;472:90–94.
91. Vanharanta S, Massague J. Origins of metastatic traits. Cancer Cell 2013;24:410–421.
92. Huang FW, Hodis E, Xu MJ, et al. Highly recurrent TERT promoter mutations in human melanoma. Science 2013;339:957–959.
93. Horn S, Figl A, Rachakonda PS, et al. TERT promoter mutations in familial and sporadic melanoma. Science 2013;339:959–961.
94. Ying Z, Li Y, Wu J, et al. Loss of miR-204 expression enhances glioma migration and stem cell-like phenotype. Cancer Res 2013;73:990–999.
95. Liang YJ, Wang QY, Zhou CX, et al. MiR-124 targets Slug to regulate epithelial-mesenchymal transition and metastasis of breast cancer. Carcinogenesis 2013;34:713–722.
96. Cooper C, Guo J, Yan Y, et al. Increasing the relative expression of endogenous non-coding Steroid Receptor RNA Activator (SRA) in human breast cancer cells using modified oligonucleotides. Nucleic Acids Res 2009;37:4518–4531.
97. Stephens PJ, Greenman CD, Fu B, et al. Massive genomic rearrangement acquired in a single catastrophic event during cancer development. Cell 2011;144:27–40.
98. Korbel JO, Campbell PJ. Criteria for inference of chromothripsis in cancer genomes. Cell 2013;152:1226–1236.
99. Baca SC, Prandi D, Lawrence MS, et al. Punctuated evolution of prostate cancer genomes. Cell 2013;153:666–677.
100. Turner EH, Lee C, Ng SB, et al. Massively parallel exon capture and library-free resequencing across 16 genomes. Nat Methods 2009;6:315–316.
101. Gnirke A, Melnikov A, Maguire J, et al. Solution hybrid selection with ultra-long oligonucleotides for massively parallel targeted sequencing. Nat Biotechnol 2009;27:182–189.
102. Levin JZ, Berger MF, Adiconis X, et al. Targeted next-generation sequencing of a cancer transcriptome enhances detection of sequence variants and novel fusion transcripts. Genome Biol 2009;10:R115.
103. Lawrence MS, Stojanov P, Polak P, et al. Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature 2013;499:214–218.
104. Kandoth C, McLellan MD, Vandin F, et al. Mutational landscape and significance across 12 major cancer types. Nature 2013;502:333–339.
105. Gartner JJ, Parker SC, Prickett TD, et al. Whole-genome sequencing identifies a recurrent functional synonymous mutation in melanoma. Proc Natl Acad Sci U S A 2013;110:13481–13486.
106. Stegh AH, Brennan C, Mahoney JA, et al. Glioma oncoprotein Bcl2L12 inhibits the p53 tumor suppressor. Genes Dev 2010;24:2194–2204.
107. Mullaney JM, Mills RE, Pittard WS, et al. Small insertions and deletions (INDELs) in human genomes. Hum Mol Genet 2010;19:R131–R136.
108. Tomlins SA, Rhodes DR, Perner S, et al. Recurrent fusion of TMPRSS2 and ETS transcription factor genes in prostate cancer. Science 2005;310:644–648.
109. Soda M, Choi YL, Enomoto M, et al. Identification of the transforming EML4-ALK fusion gene in non-small-cell lung cancer. Nature 2007;448:561–566.
110. Palanisamy N, Ateeq B, Kalyana-Sundaram S, et al. Rearrangements of the RAF kinase pathway in prostate cancer, gastric cancer and melanoma. Nat Med 2010;16:793–798.
111. Leary RJ, Kinde I, Diehl F, et al. Development of personalized tumor biomarkers using massively parallel sequencing. Sci Transl Med 2010;2:20ra14.
112. Maher CA, Kumar-Sinha C, Cao X, et al. Transcriptome sequencing to detect gene fusions in cancer. Nature 2009;458:97–101.
113. Stephens PJ, McBride DJ, Lin ML, et al. Complex landscapes of somatic rearrangement in human breast cancer genomes. Nature 2009;462:1005–1010.
114. Feng H, Shuda M, Chang Y, et al. Clonal integration of a polyomavirus in human Merkel cell carcinoma. Science 2008;319:1096–1100.
115. Herner A, Sauliunaite D, Michalski CW, et al. Glutamate increases pancreatic cancer cell invasion and migration via AMPA receptor activation and Kras-MAPK signaling. Int J Cancer 2011;129:2349–2359.
116. Wei X, Walia V, Lin JC, et al. Exome sequencing identifies GRIN2A as frequently mutated in melanoma. Nat Genet 2011;43:442–446.
117. Prickett TD, Wei X, Cardenas-Navia I, et al. Exon capture analysis of G protein-coupled receptors identifies activating mutations in GRM3 in melanoma. Nat Genet 2011;43:1119–1126.
118. Krauthammer M, Kong Y, Ha BH, et al. Exome sequencing identifies recurrent somatic RAC1 mutations in melanoma. Nat Genet 2012;44:1006–1014.
119. Davies H, Hunter C, Smith R, et al. Somatic mutations of the protein kinase gene family in human lung cancer. Cancer Res 2005;65:7591–7595.
120. Bozic I, Antal T, Ohtsuki H, et al. Accumulation of driver and passenger mutations during tumor progression. Proc Natl Acad Sci U S A 2010;107:18545–18550.
121. Parmigiani G, Boca S, Lin J, et al. Design and analysis issues in genome-wide somatic mutation studies of cancer. Genomics 2009;93:17–21.
122. Kaminker JS, Zhang Y, Waugh A, et al. Distinguishing cancer-associated missense mutations from common polymorphisms. Cancer Res 2007;67:465–473.
123. Futreal PA. Backseat drivers take the wheel. Cancer Cell 2007;12:493–494.
124. Greenman C, Wooster R, Futreal PA, et al. Statistical analysis of pathogenicity of somatic mutations in cancer. Genetics 2006;173:2187–2198.
125. Baudot A, Real FX, Izarzugaza JM, et al. From cancer genomes to cancer models: bridging the gaps. EMBO Rep 2009;10:359–366.
126. Carter H, Chen S, Isik L, et al. Cancer-specific high-throughput annotation of somatic mutations: computational prediction of driver missense mutations. Cancer Res 2009;69:6660–6667.
127. Ng PC, Henikoff S. SIFT: Predicting amino acid changes that affect protein function. Nucleic Acids Res 2003;31:3812–3814.
128. Kohli M, Rago C, Lengauer C, et al. Facile methods for generating human somatic cell gene knockouts using recombinant adeno-associated viruses. Nucleic Acids Res 2004;32:e3.
129. Downing JR, Wilson RK, Zhang J, et al. The Pediatric Cancer Genome Project. Nat Genet 2012;44:619–622.
130. Hudson TJ, Anderson W, Artez A, et al. International network of cancer genome projects. Nature 2010;464:993–998.
131. Bignell GR, Greenman CD, Davies H, et al. Signatures of mutation and selection in the cancer genome. Nature 2010;463:893–898.
132. Sieber OM, Tomlinson SR, Tomlinson IP. Tissue, cell and stage specificity of (epi)mutations in cancers. Nat Rev Cancer 2005;5:649–655.
133. Benvenuti S, Frattini M, Arena S, et al. PIK3CA cancer mutations display gender and tissue specificity patterns. Hum Mutat 2008;29:284–288.
134. Karakas B, Bachman KE, Park BH. Mutation of the PIK3CA oncogene in human cancers. Br J Cancer 2006;94:455–459.
135. Brennan CW, Werhaak RG, McKenna A, et al. The somatic genomic landscape of glioblastoma. Cell 2013;155:462–477.
136. Cancer Genome Atlas Network. Comprehensive molecular characterization of human colon and rectal cancer. Nature 2012;487:330–337.
137. Cancer Genome Atlas Network. Comprehensive molecular portraits of human breast tumours. Nature 2012;490:61–70.
138. Cancer Genome Atlas Research Network. Integrated genomic analyses of ovarian carcinoma. Nature 2011;474:609–615.
139. Cancer Genome Atlas Research Network. Comprehensive molecular characterization of clear cell renal cell carcinoma. Nature 2013;499:43–49.
140. Cancer Genome Atlas Research Network. Integrated genomic characterization of endometrial carcinoma. Nature 2013;497:67–73.
141. Weischenfeldt J, Simon R, Feuerbach L, et al. Integrative genomic analyses reveal an androgen-driven somatic alteration landscape in early-onset prostate cancer. Cancer Cell 2013;23:159–170.
142. Cancer Genome Atlas Research Network. Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia. N Engl J Med 2013;368:2059–2074.
143. Dawson SJ, Rueda OM, Aparicio S, et al. A new genome-driven integrated classification of breast cancer and its implications. EMBO J 2013;32:617–628.
144. Plass C, Pfister SM, Lindroth AM, et al. Mutations in regulators of the epigenome and their connections to global chromatin patterns in cancer. Nat Rev Genet 2013;14:765–780.
145. Shen H, Laird PW. Interplay between the cancer genome and epigenome. Cell 2013;153:38–55.
146. Yamamoto E, Suzuki H, Yamano HO, et al. Molecular dissection of premalignant colorectal lesions reveals early onset of the CpG island methylator phenotype. Am J Pathol 2012;181:1847–1861.
147. Gao J, Aksoy BA, Dogrusoz U, et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal 2013;6:pl1.
148. Bleeker FE, Bardelli A. Genomic landscapes of cancers: prospects for targeted therapies. Pharmacogenomics 2007;8:1629–1633.
149. Paez JG, Jänne PA, Lee JC, et al. EGFR mutations in lung cancer: correlation with clinical response to gefitinib therapy. Science 2004;304:1497–1500.
150. Ciardiello F, Tortora G. EGFR antagonists in cancer treatment. N Engl J Med 2008;358:1160–1174.
151. Janku F, Stewart DJ, Kurzrock R. Targeted therapy in non-small-cell lung cancer—is it becoming a reality? Nat Rev Clin Oncol 2010;7:401–414.
152. Pao W, Chmielecki J. Rational, biologically based treatment of EGFR-mutant non-small-cell lung cancer. Nat Rev Cancer 2010;10:760–774.
153. Gerber DE, Minna JD. ALK inhibition for non-small cell lung cancer: from discovery to therapy in record time. Cancer Cell 2010;18:548–551.
154. Andreyev HJ, Norman AR, Cunningham D, et al. Kirsten ras mutations in patients with colorectal cancer: the multicenter “RASCAL” study. J Natl Cancer Inst 1998;90:675–684.
155. Roth AD, Tejpar S, Delorenzi M, et al. Prognostic role of KRAS and BRAF in stage II and III resected colon cancer: results of the translational study on the PETACC-3, EORTC 40993, SAKK 60-00 trial. J Clin Oncol 2010;28:466–474.
156. Bardelli A, Siena S. Molecular mechanisms of resistance to cetuximab and panitumumab in colorectal cancer. J Clin Oncol 2010;28:1254–1261.
157. Siena S, Sartore-Bianchi A, Di Nicolantonio F, et al. Biomarkers predicting clinical outcome of epidermal growth factor receptor-targeted therapy in metastatic colorectal cancer. J Natl Cancer Inst 2009;101:1308–1324.
158. Tejpar S, Bertagnolli M, Bosman F, et al. Prognostic and predictive biomarkers in resected colon cancer: current status and future perspectives for integrating genomics into biomarker discovery. Oncologist 2010;15:390–404.
159. Gerlinger M, Rowan AJ, Horswell S, et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med 2012;366:883–892.
160. Mandel P, Metais P. [Not Available]. C R Seances Soc Biol Fil 1948;142:241–243.
161. Crowley E, Di Nicolantonio F, Loupakis F, et al. Liquid biopsy: monitoring cancer-genetics in the blood. Nat Rev Clin Oncol 2013;10:472–484.
162. Diehl F, Li M, Dressman D, et al. Detection and quantification of mutations in the plasma of patients with colorectal tumors. Proc Natl Acad Sci U S A 2005;102:16368–16373.
163. Diehl F, Schmidt K, Choti MA, et al. Circulating mutant DNA to assess tumor dynamics. Nat Med 2008;14:985–990.
164. Frattini M, Gallino G, Signoroni S, et al. Quantitative and qualitative characterization of plasma DNA identifies primary and recurrent colorectal cancer. Cancer Lett 2008;263:170–181.
165. Chan KC, Jiang P, Zheng YW, et al. Cancer genome scanning in plasma: detection of tumor-associated copy number aberrations, single-nucleotide variants, and tumoral heterogeneity by massively parallel sequencing. Clin Chem 2013;59:211–224.
166. Murtaza M, Dawson SJ, Tsui DW, et al. Non-invasive analysis of acquired resistance to cancer therapy by sequencing of plasma DNA. Nature 2013;497:108–112.
167. Bardelli A, Corso S, Bertotti A, et al. Amplification of the MET receptor drives resistance to anti-EGFR therapies in colorectal cancer. Cancer Discov 2013;3:658–673.
168. Higgins MJ, Jelovac D, Barnathan E, et al. Detection of tumor PIK3CA status in metastatic breast cancer using peripheral blood. Clin Cancer Res 2012;18:3462–3469.
169. Leary RJ, Sausen M, Kinde I, et al. Detection of chromosomal alterations in the circulation of cancer patients with whole-genome sequencing. Sci Transl Med 2012;4:162ra154.
170. Misale S, Yaeger R, Hobor S, et al. Emergence of KRAS mutations and acquired resistance to anti-EGFR therapy in colorectal cancer. Nature 2012;486:532–536.
171. Diaz LA Jr, Williams RT, Wu J, et al. The molecular evolution of acquired resistance to targeted EGFR blockade in colorectal cancers. Nature 2012;486:537–540.
172. Robbins PF, Lu YC, El-Gamil M, et al. Mining exomic sequencing data to identify mutated antigens recognized by adoptively transferred tumor-reactive T cells. Nat Med 2013;19:747–752.
173. Hunter C, Smith R, Cahill DP, et al. A hypermutation phenotype and somatic MSH6 mutations in recurrent human malignant gliomas after alkylator chemotherapy. Cancer Res 2006;66:3987–3991.
174. Cahill DP, Levine KK, Betensky RA, et al. Loss of the mismatch repair protein MSH6 in human glioblastomas is associated with tumor progression during temozolomide treatment. Clin Cancer Res 2007;13:2038–2045.
175. Engelman JA, Zejnullahu K, Mitsudomi T, et al. MET amplification leads to gefitinib resistance in lung cancer by activating ERBB3 signaling. Science 2007;316:1039–1043.
176. Gorre ME, Mohmmed M, Ellwood K, et al. Clinical resistance to STI-571 cancer therapy caused by BCR-ABL gene mutation or amplification. Science 2001;293:876–880.
177. Heinrich MC, Corless CL, Blanke CD, et al. Molecular correlates of imatinib resistance in gastrointestinal stromal tumors. J Clin Oncol 2006;24:4764–4774.
178. Shepherd FA, Rodrigues Pereira J, Ciuleanu T, et al. Erlotinib in previously treated non-small-cell lung cancer. N Engl J Med 2005;353:123–132.
179. Kobayashi S, Boggon TJ, Dayaram T, et al. EGFR mutation and resistance of non-small-cell lung cancer to gefitinib. N Engl J Med 2005;352:786–792.
180. Kwak EL, Sordella R, Bell DW, et al. Irreversible inhibitors of the EGF receptor may circumvent acquired resistance to gefitinib. Proc Natl Acad Sci U S A 2005;102:7665–7670.
181. Pao W, Miller VA, Politi KA, et al. Acquired resistance of lung adenocarcinomas to gefitinib or erlotinib is associated with a second mutation in the EGFR kinase domain. PLoS Med 2005;2:e73.
182. Shih JY, Gow CH, Yang PC. EGFR mutation conferring primary resistance to gefitinib in non-small-cell lung cancer. N Engl J Med 2005;353:207–208.
183. Bell DW, Gore I, Okimoto Ra, et al. Inherited susceptibility to lung cancer may be associated with the T790M drug resistance mutation in EGFR. Nat Genet 2005;37:1315–1316.
184. Inukai M, Toyooka S, Ito S, et al. Presence of epidermal growth factor receptor gene T790M mutation as a minor clone in non-small cell lung cancer. Cancer Res 2006;66:7854–7858.
185. Daub H, Specht K, Ullrich A. Strategies to overcome resistance to targeted protein kinase inhibitors. Nat Rev Drug Discov 2004;3:1001–1010.
186. Choi YL, Soda M, Yamashita Y, et al. EML4-ALK mutations in lung cancer that confer resistance to ALK inhibitors. N Engl J Med 2010;363:1734–1739.
187. Flaherty KT, Puzanov I, Kim KB, et al. Inhibition of mutated, activated BRAF in metastatic melanoma. N Engl J Med 2010;363:809–819.
188. Nazarian R, Shi H, Wang Q, et al. Melanomas acquire resistance to B-RAF(V600E) inhibition by RTK or N-RAS upregulation. Nature 2010;468:973–977.
189. Pompetti F, Spadano A, Sau A, et al. Long-term remission in BCR/ABL-positive AML-M6 patient treated with Imatinib Mesylate. Leuk Res 2007;31:563–567.
190. Druker BJ, Builhot F, O’Brien SG, et al. Five-year follow-up of patients receiving imatinib for chronic myeloid leukemia. N Engl J Med 2006;355:2408–2417.